The Department of Statistics of Veer Narmad South Gujarat University was established in 1972 as a joint Department of Mathematics and Statistics. The mathematics section later got separated as a separate department.
In 1996, the department was also entrusted with the responsibility of running the Computer Science teaching programme (MCA, DCA) of the University and also with the administration of Central University Computer Centre. From 1996 to 2003 a significant part of Departmental resources and offices were devoted to consolidate the Computer Science teaching programmes and the Computer Centre, so as to achieve a situation in which the department could move a proposal for creation of a separate department of Computer Science. The proposal of the department was implemented by the University from academic the year 2003-04.
Continuing the efforts of the University and the department, to serve the society through knowledge, the department successfully started a Higher Payment PG Programme, M.Sc. (Applied Statistics) from the academic year 2007-08 and has offered a certificate course on "PYTHON FOR STATISTICS" from March 2022.From the year 2022 department also start the certificate course on "Communicative English for Career(CEC)" and in 2023 introduced a certificate courses on "Advance Excel for Business Analytics" and "Statistical Data Analysis using SPSS"
Vision
To be an institute of excellence in higher and technical education segment, sensitive to its regional needs and changing global realities.
Mission
In pursuance of its vision, Veer Narmad South Gujarat University offers different programmes through well designed curricular, co-curricular and extracurricular activities. Undertakes research and reaches out to society at large with various extension activities, in order to empower its stakeholders for the world class skills in terms of research and enquiry, creativity, innovation, capacity to use high technology and value based ethical leadership.
Objectives
To be useful to the society as a whole, including all kinds of industries, and all those fields where Statistics is used.
▶ Department of Statistics
Department of Statistics
Veer Narmad South Gujarat University
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
Research
Since 2004, there have been less than 3 permanent faculties who were Ph. D. guides too; the faculties of the department are contributing to good research. Some full time faculties are also offered Ph. D. guide ship within last few years. Main research areas offered by the departments are Sampling Theory, Operations Research, Statistical Quality Control, Applied Statistics and Econometrics. The faculty members and their research scholars have published many research papers in reputed journals. Students as well as faculties have also won best paper awards as well as best presentation awards at different national and international seminars and conferences. The department also organizes seminars and conferences on emerging trend and new developments in the research subject areas.
Ph. D Awarded:29
M. Phil Awarded: 30
Research paper: 288
Seminar/conference organized jointly with UG colleges affiliated with the university:
Department level workshop/Training Program: 03
State level: 02
National level: 04
International Conference = 01
Webinar organized: 01
National Level Online Quiz organized:02
Achievements of Faculties in paper presentation : 04
S. R. Sheikh and R. D. Patel (Sept. 2015): Inventory Model with Different Deterioration Rates under Linear Demand and Time Varying Holding Cost; International Journal of Mathematics and Statistics Invention (IJMSI) (ISSN No. 2321-4759), Vol. 3, No. 6, pp. 36-42.
S. R. Sheikh and R. D. Patel (Sept. 2015): Deteriorating Items Inventory Model with Different Deterioration Rates and Shortages; International J. Latest Technology in Engineering, Management and Applied Sciences (IJLTEMAS) (ISSN No. 2278-2540), Vol. IV, No. XI, pp. 46-51. (Impact Factor – 1.356)
S. R. Sheikh and R. D. Patel (Nov. 2015)Inventory Model with Different Deterioration Rates with Stock and Price Dependent Demand under Time Varying Holding Cost; International Referred Journal of Engineering and Science (IRJES) (ISSN No. 2319-1821), Vol. 4, No. 11, pp. 01-12.
R. D. Patel and D. M. Patel (Dec. 2015): Inventory Model with Different Deterioration Rates under Exponential Demand, Inflation and Permissible Delay in Payments; (jointly with) International J. Latest Technology in Engineering, Management and Applied Sciences (IJLTEMAS) (ISSN No. 2278-2540), Vol. IV, No. XII, pp. 01-11. (Impact Factor – 1.356)
S. R. Sheikh and R. D. Patel (Dec. 2015): Inventory Model with Different Deterioration Rates with Stock and Price Dependent Demand under Time Varying Holding Cost and Shortages; International Journal of Mathematics and Statistics Invention (IJMSI) (ISSN No. 2321-4759), Vol. 3, No. 8, pp. 01-14.
S. R. Sheikh and R. D. Patel (Feb. 2016): Production Inventory Model with Different Deterioration Rates under Linear Demand; IOSR Journal of Engineering, (IOSR JEN) (ISSN No. 2278-8719), Vol.6, No. 2, pp. 71-75.
S. R. Sheikh and R. D. Patel (March 2016): Production Inventory Model with Different Deterioration Rates under Shortages and Linear Demand; International Referred Journal of Engineering and Science (IRJES) (ISSN No. 2319-1821), Vol.5, No. 3, pp. 1-7.
R. D. Patel and D.M. Patel (March 2016): Inventory Model with Different Deterioration Rates under Exponential Demand, Shortages, Inflation and Permissible Delay in Payments; International Journal of Mathematics and Statistics Invention (IJMSI) (ISSN No. 2321-4759), Vol. 4, No. 3, pp. 27-40.
Tarulata Patel and R. D. Patel (April 2016): Duality for Semi- Infinite Multiobjective Fractional Programming Problems Involving Generalized (Hp, R)-Invexity; International Refereed Journal of Engineering and Science (IRJES) (ISSN (Online) 2319-183X, (Print) 2319-1821) Vol. 5, Issue 4, pp. 07-15
Tarulata Patel and R. D. Patel (April 2016): Higher-Order (F, α, β, ρ, d) –Convexity for Multiobjective Programming Problem; International Journal of Mathematics and Statistics Invention (IJMSI) (E-ISSN: 2321 – 4767 P-ISSN: 2321 – 4759) Vol. 4, Issue 4, pp.13-19
R. D. Patel and D.M. Patel (June 2017): Inventory Model with Different Deterioration Rates with Shortages, Time and Price Dependent Demand under Inflation and Permissible Delay in Payments; Global Journal of Pure and Applied Mathematics (GJPAM) (ISSN No. 0973-1768), Vol. 13, No. 6, pp. 1499-1514.
S. R. Sheikh and R. D. Patel (June 2017): Two Warehouse Inventory Model with Different Deterioration rates under Linear Demand and Time Varying Holding Cost; Global Journal of Pure and Applied Mathematics (GJPAM) (ISSN No. 0973-1768), Vol. 12, No. 6, June 2017, pp. 1515-1525.
B. T. Naik and R. D. Patel (June 2017): Inventory Model with Different Deterioration Rates for Imperfect Quality Items and Linear Demand; Global Journal of Pure and Applied Mathematics (GJPAM) (ISSN No. 0973-1768), Vol. 13, No. 6, June 2017, pp. 1537-1552.
Raman Patel (June 2017): Different Deterioration Rates Production Inventory Model with Time and Price Dependent Demand under Inflation and Permissible Delay in Payments; International Journal of Computational and Applied Mathematics (IJCAM) (ISSN No. 1819-4966), Vol. 12, No. 2, pp. 425-439.
B. T. Naik and Raman Patel (June 2017): Deteriorating Items Inventory Model with Different Deterioration Rates for Imperfect Quality Items and Shortages; International Journal of Computational and Applied Mathematics (IJCAM) (ISSN No. 1819-4966), Vol. 12, No. 2, June 2017, pp. 273-284.
Raman Patel (July 2017): Inventory model for imperfect quality items with different deterioration rates under inflation and permissible delay in payments; International Journal of Latest Technology in Engineering, Management and Applied Sciences (IJLTEMAS) (ISSN No. 2278-2540 ), Vol. VI, Issue VII, pp. 173-179.
S. R. Sheikh and Raman Patel (Aug. 2017): Two Warehouse Inventory Model with Different Deterioration rates under Time Dependent Demand and Shortages; Global Journal of Pure and Applied Mathematics (GJPAM) (ISSN No. 0973-1768), Vol. 13, No. 8, pp. 3951-3960.
B. T. Naik and Raman Patel (Sept. 2017): Inventory Model with Different Deterioration Rates for Imperfect Quality Items under Price and Time Dependent Demand; Global Journal of Pure and Applied Mathematics (GJPAM) (ISSN No. 0973-1768), Vol. 13, No. 9, pp. 6907-6918.
Raman Patel (Sept. 2017): Inventory Model for Imperfect Quality Items under Different Deterioration Rates and Shortages considering Price and Time Dependent Demand with Inflation and Permissible Delay in Payments; International Journal of Advanced Research and Review (IJCARR) (ISSN No.2455-7277), Vol. 2, No. 9, pp. 82-100.
S. R. Sheikh and Raman Patel (Sept. 2017): Deteriorating Items Production Inventory Model with Different Deterioration rates under Stock and Price Dependent Demand; International Journal of Statistics and Systems (IJSS) (ISSN No. 0973-2675), Vol. 12, No. 3, September 2017, pp. 607-618.
S. R. Sheikh and Raman Patel (Sept. 2017): Production Inventory Model for Deteriorating Items with Different Deterioration rates under Stock and Price Dependent Demand and Shortages; International Journal of Statistics and Systems (IJSS) (ISSN No. 0973-2675), Vol. 12, No. 3, pp. 631-643.
S. R. Sheikh and R. D. Patel (2018): Two warehouse production inventory model with different deterioration rates under linear demand and time varying holding cost; International Journal of Theoretical and Applied Sciences (ISSN No. 2249-3247 ) , Vol. 10, No. 1, 2018, pp. 71-78.
R. D. Patel (2018): Different deterioration rates two warehouse inventory model with time and price dependent demand under inflation and permissible delay in payments; International Journal of Theoretical and Applied Sciences (ISSN No. 2249-3247 ), Vol. 10, No. 1, pp. 53-65.
B. T. Naik and Raman Patel (2018): Inventory model for imperfect quality and repairable items with varying deterioration; International Journal of Theoretical and Applied Sciences (ISSN No. 2249-3247 ), Vol. 10, Issue 1, 2018, pp. 185-190.
B. T. Naik and Raman Patel (Feb. 2018):Imperfect quality items inventory model under different deterioration rates and shortages with price and time dependent demand International Journal of Latest Trends in Engineering and Technology (IJLTET) (e-ISSN : 2278-621X , p-ISSN : 2319-3778 ), Vol. 9, Issue 4, February 2018, pp. 6-13.
R. D. Patel (March 2018): Different deterioration rates production inventory model with two warehouses under shortages, inflation and permissible delay in payments; International Journal of Latest Trends in Engineering and Technology (ISSN No.2249-3237 ), Vol. 10, No. 1, pp. 190-201.
B. T. Naik and Raman Patel (March 2018): Two Storage Facilities Inventory Model for Defective Items with Different Rates of Deterioration and Linear Demand; International Journal of Current Trends in Engineering and Technology (IJCTET) (ISSN No. 2395-3152), Vol. 4, Issue 2, pp. 92-96.
B. T. Naik and Patel Raman (April 2018): Different deterioration rates two warehouse defective items inventory model with time and price dependent demand International Journal of Engineering, Sciences and Mathematics (IJESM) (ISSN No. 2320-0294 ), Vol. 7, Issue 4, pp. 390-399.
Jayshree Pandey and R. D. Patel (Feb. 2019): Deteriorating items supply chain inventory model for single vendor single buyer under exponential demand; Journal of Emerging Technologies and Innovative Research (ISSN No. 2349-5162), Vol. 6(2). Pp. 1-7, Feb – 2019.
Jayshree Pandey and R. D. Patel (April 2019): Supply chain inventory model for single vendor single buyer when shortages are allowed to buyer; Journal of Emerging Technologies and Innovative Research (ISSN No. 2349-5162), Vol. 6(4). Pp. 309-315.
Jayshree Pandey and R. D. Patel (May 2019): Deteriorating items supply chain inventory model for multiple buyers and single vendor under exponential demand; Journal of Emerging Technologies and Innovative Research (ISSN No. 2349-5162), Vol. 6(5). Pp. 686-692.
R. D. Patel and Jiten Patel (June 2019): Different deterioration rates deteriorating items for one vendor one buyer supply chain inventory model under linear demand; International Journal of Engineering, Applied and Management Sciences Paradigms (IJEAMS) (ISSN No. 2320-6608), Vol. 54(3). pp. 450-456, June – 2019. (Impact Factor 2.50)
R. D. Patel and Jiten Patel (August 2019): Deteriorating items supply chain inventory model for single vendor single buyer under time and price dependent demand; International Journal of Engineering Research and Technology (IJERT) (ISSN No. 0974-3154), Vol. 12(8). pp. 1307-1312.
R. D. Patel and Jiten Patel (Oct. 2019): Different deterioration rates two storage facilities deteriorating items inventory model under time and price dependent demand for single buyer single vendor (jointly with Jiten Patel); International Journal of Scientific and Technology Research (IJSTR) (ISSN No. 2277-8616), Vol. 8(10). pp. 1824-1829.
H. M. Shah and A. J. Rajyaguru (March,2015):Statistical Comparison of Share of Occupations In Bank Group Wise And Population Group Wise Credit Utilisation Between Gujarat With Other Selected States, , Indian Journal of Statistics And Application (Issn- 2278-1102), Vol.- 3, Issue-1&2, Pp.- 64 To 76
M. C. Shingala and A. J. Rajyaguru(Nov., 2015): Comparison of Post Hoc Tests For Unequal Variance, International Journal of New Technologies In Science And Engineering, Vol.-2, Issue-5, Pp.-22-33
H. M. Shah and A. J. Rajyaguru(Dec., 2015): Statistical Comparison of Some Banking Parameters Between Gujarat And Other Selected States, Indian Journal of Statistics And Application Issn- 2278-1102 (Issn- 2278-1102), Vol- 4, Issue:1, Pp.-18 To 30
Shah H. M., Rajyaguru A. J and Kazi G. (Dec., 2015), Vitamin D: Reality or Hype?: A Case Study of Combined Data of Surat, Ahmedabad And Vadodaracity. Int J. Recent Sci Res. 9(1), Pp. 23420-23426. Doi: Http://Dx.Doi.Org/10.24327/Ijrsr.2018.0901.1465
Rajyaguru A. J, Shah H. M. and Kazi G. (Dec.,2015): Vitamin B12 Deficiency: Case Study of Vadodara, Ahmedabad And Surat City, International Journal Of Research And Scientific Innovation, Volume 3 Issue 1,Issn : 2321 – 2705, (Impact Factor 2.08)
S. D. Patel, and A. J. Rajyaguru(Dec.,2015): "An Econometric Analysis of Cereal Consumption In Rural India, International Journal of Research And Scientific Innovation, Vol.-3, Issue-1, Pp.-
D. R. Radadiya and A. J. Rajyaguru (Dec., 2015): A Statistical Study of Increasing Crime Rate Against Women In India, Gujarat And Major Cities Of Gujarat. International Journal of Research And Scientific Innovation, Vol.-3, Issue-1, Pp.312-317.
S. Dange, K. Manoj and A. J. Rajyaguru (Jan.-2016): Statistical Analysis of Water Quality Data For The Purna River Estuary of Navsari, Gujarat,, International Journal of Multidisciplinary Research Review, Vol.1, Issue – 1, Pp.75-81
M. C. Shingala, and A. J. Rajyaguru. (2017). Nonparametric Post Hoc Test With Adjusted P Value. International Education and Research Journal (IERJ), 3(7).
S. D. Patel, A. J. Rajyaguru and H. M. Shah (Nov., 2020): Patterns of Consumer Service Consumption – A Comparative Study of Rural Areas of Major States Including Gujarat And India, Journal of Social Science – Issn: 2279-0241, Published By Knowledge Consortium of Gujarat, Issue 45, Pp.-1-6
Shital S. Patel (March 2015): Inventory model for deteriorating items with stock dependent demand under partial backlogging and variable selling price, International Journal of Scientific Research (ISSN: 2277-8179), Vol.4 Issue 3, pp. 375-379 (Impact factor 1.8651)
Raman Patel, Reena U. Parekh and Shital S. Patel (April 2015): Deteriorating Items Production Inventory Models with Two Warehouses under Shortages, Inflation and Permissible Delay in Payments, International Journal of Latest Technology in Engineering, Management and Applied Sciences Research and Scientific Innovation Society, (ISSN: 2278-2540), Vol. IV, No. IV, pp. 55-66.
Shital S. Patel and R. D. Patel (Nov. 2015): Optimal credit period and replenishment time for credit and stock dependent demand inventory model, Proceedings of International Conference on Industrial Engineering, Department of Mechanical Engineering, S.V. National Institute of Technology, Surat, India, (ISBN:978-93-84935-56-6) pp. 748-752
R. D. Patel and Shital S. Patel (Nov. 2015): Inventory model with different deteriorating rates under exponential demand, inflation and time varying holding cost, Proceedings of International Conference on Industrial Engineering, Department of Mechanical Engineering, S.V. National Institute of Technology, Surat, India, (ISBN:978-93-84935-56-6) pp. 1418-1423
Shital S. Patel (Aug. 2017): Inventory model with different deterioration rates for imperfect quality items and inflation considering price and time dependent demand under permissible delay in payments, International Journal of Latest Technology in Engineering, Management and Applied Sciences Letter (ISSN: 2278-2540 ), Vol. 6, No. 7, pp. 1-9
Shital S. Patel (2017): Inventory Model with Different Deterioration Rates with Time and Price Dependent Demand under Inflation and Permissible Delay in Payments, International Journal of Computational and Applied Mathematics Letter( ISSN: 1819-4966), Vol. 12, No. 2, pp. 207-223
Shital S. Patel (2017): Production Inventory Model for Deteriorating Items With Different Deterioration Rates Under Stock and Price Dependent Demand And Shortages Under Inflation and Permissible Delay in Payments, Global Journal of Pure and Applied Mathematics (ISSN: 0973-1768) Vol 13 No. 7, pp 3687-3701
Shital S. Patel (March 2018): Different Deterioration Rates Two Warehouse Inventory Model with Time and Price Dependent Demand under Inflation and Permissible Delay in Payments and Shortages, International Journal of Engineering Research and Development (e-ISSN 2278-067X & p-ISSN 2278-800X), Vol. 14 Issue 3, Ver. II, pp. 1-11
Shital S. Patel (April 2018 ): Different Deterioration Rates Two Warehouse Production Inventory Model with Time and Price Dependent Demand under Inflation and Permissible Delay in Payments, International Journal of Engineering Science Invention, (e-ISSN: 2319-6726 & p-ISSN:2319-6726) Vol. 7, Issue 4, pp. 53-62
Shital S. Patel (Sept. 2019 ): An EPQ Model For Variable Production Rate With Exponential Demand And Unsteady Deterioration, International Journal of Scientific & Technology Research (ISSN 2277-8616), Vol. 8, Issue 09, pp. 1206-1209 (Impact Factor: 3.023)
Shital S. Patel (Jan. 2021): Imperfect Quality And Repairable Items Inventory Model Under Unsteady Deterioration And Three Tiered Pricing, International Journal of Science Technology and Management (e-ISSN:2394-1537 & p-ISSN:2394-1529), Vol. 10, Issue 01, pp. 01-12 (Impact Factor- 4.6)
Shital S. Patel (Jan. 2021): Comparison Of Inventory Models Under Non-Instantaneous Deterioration Rate With Probabilistic Demand And Shortages, PARIPEX-INDIAN JOURNAL OF RESEARCH( ISSN(P): 2250-1991), Vol. 10, Issue 01, pp. 21-23 (Impact Factor 6.941)
Shital S. Patel (May 2021): Deteriorating Items Inventory Model for Defective Items under Different Deterioration with Three Tired Prices and Time Dependent Demand, International Journal of Innovative Research in Science, Engineering and Technology ( E-ISSN 2319-8753 & P-ISSN 2320-6710 ), Vol. 10, Issue. 5, (Impact factor 7.512)
Shah Hemali M, Rajyaguru Arti J., Kazi Girish J. (2022): Vitamin D status: Acase study of Surat city, International Journal of Health Science, ISSN 2550-6978, E-ISSN 2550-696X
Presentations
R. D. Patel: Two Warehouses Deteriorating Items Production Inventory Models under Shortages, inflation and Permissible Delay in Payments; Presented at National Conference on ‘Recent Trends in Mathematical and Computational Sciences (NCRTMCS-2015) during 3-4th, January, 2015 at Department of Statistics, Bhagalpur University, Bhagalpur, Bihar.
R. D. Patel:Deteriorating Items Inventory Model with Stock and Price Dependent Demand Under Time Varying Holding Cost; Presented at International Conference on ‘Advances in Management and Decision Sciences during 11-12th, July, 2015 at School of Management, Gautam Buddha University, Greater Noida, Uttar Pradesh.
R. D. Patel:Inventory Model with Different Deteriorating Rates under Exponential Demand, Inflation and Permissible Delay in Payments; Presented at National Conference on ‘Recent Innovations in Applied Sciences and Humanities (NCASH-2015) during 10th, October, 2015 at Department of Applied Sciences and Humanities, Rawal Institution of Engineering and Technology, Faridabad, Haryana.
R. D. Patel:Inventory Model with Different Deteriorating rates under Exponential Demand and Time Varying Holding Cost; Presented at 3rd International Conference on ‘Industrial Engineering (ICIE 2015) during 26-28th, November, 2015 at SVNIT, Surat.
R. D. Patel:Production Inventory Model with Different Deterioration Rates under Inflation and Permissible Delay in Payments; Presented at International Conference on ‘Challenges and Opportunities before 21stCenturyin Indian in the Fields of Social Sciences, Science, Management and Technology’ during 6-7th, February, 2016 at Rajarshi Chhatrapati Shahu College, Kolhapur, Maharashtra.
R. D. Patel:Modeling and Analysis of Inventory Models with Different Deterioration rates under Exponential Demand, Inflation and Permissible Delay in Payments: Presented at International Conference on ‘Science: Emerging Scenario and Future Challenges” (SESFC 2016) during 11-12th, June 2016 Organized by Him Science Congress Association, Himachal Pradesh at Hotel Inclover, Dharamshala
R. D. Patel:Modeling and Analysis of Inventory Models under Different Deterioration rates with Stock Dependent Demand and Variable Selling Price; Presented at National Conference on ‘Emerging Trends in Global Business Management’ during 3rd December 2016, Organized by Department of Business and Industrial management, G.H. Bhakta Management Academy, Veer Narmad South Gujarat University, Surat.
R. D. Patel:Inventory Model with Different Deterioration Rates with Stock Dependent Demand, Shortages, Variable Selling price, Inflation and Permissible Delay Payments; Presented at International Conference on ‘Interdisciplinary Mathematics, Statistics and Computational Techniques during 22-24th, December 2016 at Department of Mathematics & Statistics, Manipal University, Jaipur.
R. D. Patel:Different Deterioration rates Production Inventory Model with Exponential Demand, Shortages, Inflation and Permissible Delay in Payments; Presented at 8th International Conference on ‘Strengthening Strategies, Shaping Policies and Empowering Personnel: Key to Organizational Competitiveness’ during 7-9th, January, 2017 at PRESTIGE Institute of Management, Gwalior.
R. D. Patel:Production Inventory Model with Deterioration rates under Time and Price Dependent Demand, Inflation and Permissible Delay in Payment; Presented at National Conference on ‘Mathematical sciences for Development of Science and Technology” during 26-27th, February 2017, organized by Department of Mathematics, GLA University, Mathura.
R. D. Patel:Two Level Production Inventory Model with Time and Price Dependent Demand under Inflation and Permissible Delay in Payments;(jointly with D.M. Patel) Presented at International Conference on ‘Theory and Applications of Statistics and Information Sciences TASIS-2018 in conjunction with XXXVII Annual Convention of Indian Society for Probability and Statistics held at Department of Statistics, Bharathiar University, Coimbatore, Tamilnadu, India during 5-7th,January 2018.
R. D. Patel:Two Warehouse Production Inventory Model for Deteriorating Items with Time and Price Dependent Demand under permissible Delay in Payments; Presented at 2nd International Conference of VijnanaParishad of India on ‘Recent Trends of Computing in Mathematics, Statistics and Information Technology held at Department of Mathematical Sciences & Computer Application, Bundelkhand University, Jhansi, U.P., India during 9-11th, March 2018.
R. D. Patel:Two Storage Facility Inventory Model for Defective and Repairable Items under Different Deterioration Rates; Presented at National Conference on ‘Intercepting Emerging Domains in Mathematical Sciences and 22nd Prof. P.D. Verma Memorial Lecture (NCIEDMS & PDVML-2018)” during 7 September, 2018, organized by Department of Mathematics, University of Rajasthan, Jaipur.
R. D. Patel:Two Warehouse Inventory Model for Defective and Repairable Items under Inflation and Permissible Delay in Payments; Presented at Prof. P.C. Vaidya National Conference on ‘Mathematical Sciences” during 24-25th, December 2018, organized by Department of Mathematics, St. Xavier’s College, Ahmedabad & Gujarat GanitMandal.
R. D. Patel: Two Storage Facility Inventory Model for Defective and Repairable Items with Time and Price Dependent Demand under Permissible Delay in Payments; Presented at World Business ‘n Economy Congress during 15-17th, February2019 at Sage University, Indore.
R. D. Patel: Deteriorating Items Supply Chain Inventory Model for Single Vendor Single Buyer with Shortages under Time and Price Dependent Demand; Presented at National Conference on “Mathematical Modelling, Methods and Computation in Science and Engineering (MMMCSE-2019) held at Department of Mathematics, NIT Raipur during 19-20thOctober 2019.
R. D. Patel: Deteriorating Item Supply Chain Inventory Model for Single Vendor Multiple Buyers under Time and Price Dependent Demand (Jointly with Jiten Patel); Presented at International Conference on ‘Recent Advances in Statistics and Data Science for Sustainable Development (RASDSSD-2019) in conjunction with XXXIX Annual Convention of Indian Society for Probability and Statistics (ISPS) held at Department of Statistics, Post Graduate Department of Statistics, Utkal University, VaniVihar, Bhubaneswar – 751004, Odisha, during 21-23th December, 2019.
R. D. Patel: Deteriorating Items Inventory Model For Defective Items Under Different Deterioration With Three Tired Price Dependent Demand; Presented at Online International Conference on Computational Sciences : Modelling, Computing and Soft Computing (CSMCS – 2020)held at Department of Mathematics, NIT Calicut during 10-12thSeptember 2020.
Shital S. Patel: “An inventory model for deteriorating items with time varying demand, partial backlogging under inflation & permissible delay in payment” One day state level seminar on “RECENT STATISTICAL TRENDS IN BUSINESS AND MANAGEMENT (RSTBM)” on 1st Feb-2015, organized by Dept. of Statistics, VNSGU, Surat and UCCC & SPBCBA & UACCAIT, Managed by Udhna Academy Education Trust, Udhna-Surat.
Shital S. Patel: “Optimal Credit Period and Replenishment time for credit and stock Dependent Demand Inventory Model” 57th National Convention of Indian Institution of Industrial Engineering & 3rd International Conference on Industrial Engineering (ICIE 2015) during 26-28 Nov. 2015, hosted by IIE, Surat Chapter and SVNIT, Surat.
Shital S. Patel: “Multi-item inventory model with different demand and deterioration rates under capacity constraint” National seminar on “ Role of Statistics in Current Scenario” on 27th March 2016,organized by Department of Statistics, Veer Narmad South Gujarat University, Surat.
Shital S. Patel: “Deteriorating items Inventory Model under shortages for optimal credit period and replenishment time with credit and stock dependent demand”, International Conference on “ Science: Emerging Secenario and Future Challenges” during 11-12 June 2016,organized by HIM Science Congress Association, Dharamshala, Himachal Pradesh, India.
Shital S. Patel: “Comparison of production inventory model for deteriorating items with demand dependent production rate under with and without shortages”, One week short training Programme on “Nonlinear Analysis, Computations using MATLAB, Mathematica, MAPLE, and CPLEX with Applications in Engineering & Sciences” during30 Sept. – 04 Oct. 2016,organized by department of Applied Mathematics and Humanities, SardarVallabhbhi National Institute of Technology, Surat.
Shital S. Patel: “An inventory model for deteriorating items with two types of retailers under different demand”, International Conference on Interdisciplinary Mathematics, Statistics and Computational Techniques of Forum for Interdisciplinary Mathematics during 22-24 December, 2016, organized in association with Manipal University, Jaipur.
Shital S. Patel: “Production Inventory model with different deterioration rates with time and price dependent demand, shortages under inflation and permissible delay in payment”, One day National Seminar on “Role of Statistics in Research” on5th March 2017,organized by J. Z. Shah Arts and H. P. Desai Commerce College, Amroli, Surat and Department of Statistics, Veer Narmad South Gujarat university, Surat.
Shital S. Patel: “Inventory Modeling on Fresh Seasonal Products with time varing Deterioration Rate and stock dependent demand”, National Conference on “ Mathematical Sciences for Development of Science and Technology” during 26-27 February 2017, organized by Department of Mathematics, GLA University, Mathura.
Shital S. Patel: “Multi-item deterministic inventory model with capacity constraint and shortages under different demand and deterioration via Lagrange Method”, One day National Seminar on “Advances in Statistics and its Applications” on 9thJulty 2017, jointly organized by Smt. C. D. J. Rofel Arts and Smt. I. S. R. A. Rofel Commerce College, Vapi and Department of Statistics, Veer Narmad South Gujarat university, Surat.
Shital S. Patel: “Two warehouse inventory model with different deterioration rates with variable holding cost and shortages”, International Conference on Applied Mathematical Models during 04-06 January 2018, organized by Department of Mathematics, PSG College of Technology, Coimbatore.
Shital S. Patel: “Inventory model under two warehouse with different deterioration rates”, International Conference on “ Theory and Applications of Statistics and Information Sciences” during 05-07 January 2018, organized by Department of Statistics, Bharathiar University, Coimbatore.
Shital S. Patel: “Two Storage Facility Inventory modal for different deterioration rates under time and price dependent demand with delay in payment”, One day National Seminar on “Statistics: Recent Innovations and Future Challenges” on 29 January 2018, organized by Sir K. P. College of Commerce, Athwalines, Surat and Department of Statistics, Veer Narmad South Gujarat university, Surat.
Shital S. Patel: “An EPQ model for variable production rate with Exponential Demand and Unsteady Deterioration”, 1st International Conference of Association of Inventory Academicians and Practitioners on “ Emerging Trends in Inventory, Supply Chain and Reliability Modeling” during 21-23 Dec. 2018, in association with Department of Operational Research, University of Delhi, Delhi and Department of Mathematics, Gujarat University, Ahmedabad.
Shital S. Patel: “Mathematical Characteristics of Deteriorating Items Inventory Model Under Different Probabilistic Demand”, International Conference on “International Conference on Gravitation, Cosmology &Mathematical Physics ” during 04-07 April 2019, organized by Department of Mathematics , GLA University, Mathura.
Shital S. Patel: “Inventory Model with Different Probabilistic Deterioration Rates under Shortages and Time Varying Demand”, ICSSR Sponsored Two Days International Conference “ Emerging Issue In Development For Future Generation” during 12-13 February 2020, Jointly Organized by J. Z. Shah Arts and H. P. Desai Commerce College, Amroli& The International Institute For Development Studies (IIDS), Australia at J. Z. Shah Arts and H. P. Desai Commerce College, Amroli, Surat.
Shital S. Patel: “Statistical Quality Control of Multi-Item Inventory Model with Different Demand and Deterioration Rates Under Capacity Constraint”, Online INTERNATIONAL CONFERENCE ON RECENT TRENDS IN MATHEMATICAL SCIENCES (ICRTMS-2021) during 11-12 Dec. 2021, organized by HIMACHAL GANITA PARISHAD (HGP), Shimla.
Shital S. Patel: “Study on Inventory Model on Effect of Partial Backlogging Under Unsteady Deterioration Rates with Exponential Demand”, during 18-19 Dec. 2021, Online International Conference on “Recent Advancement in Science, Engineering, Management & Humanities” in association with Advance Research Educational Society (ARES) organized by Government College Degana, Nagaur (Rajasthan).
J. J. Pandey: Supply chain inventory model for single vendor single buyer when shortages are allowed to buyer; Presented at 11th National Science Symposium 2019 on “Recent trends in science and technology” on 3rd February 2019 organized by Christ college, Rajkot.
J. J. Pandey: Supply chain inventory model for deteriorating items with exponential demand for single vendor two buyers; Presented at Fourth International conference on “Statistics for twenty first century” during 13-15th December 2018 organized by Department of Statistics, University of Kerela, Trivendrum.
J. J. Pandey: Deteriorating items supply chain inventory model for single vendor single buyer under exponential demand; Presented at National Seminar on “Statistics: Recent innovation and future challenges” on 28th January 2018 organized by Sir K.P. College of commerce and Department of Statistics, Veer Narmad South Gujarat University, Surat.
J. J. Pandey: A simultaneous equation model of economic growth in India; Presented at National Seminar on “Advances in Statistics and Its Applications” on 9th July 2017 organized by Rofel commerce College Vapi and Department of Statistics, Veer Narmad South Gujarat University, Surat.
J. J. Pandey: Optimal Buyer-Vendor Inventory Model Policy in Collaborative Supply Chain; Presented at National Seminar on “ Role of Statistics in Research” on 5th March 2017 organized by J. Z. Shah Commerce College Amroli and Department of Statistics, Veer Narmad South Gujarat University, Surat.
J. J. Pandey: Relationship between Savings and Economic Growth: Co- integration and Causality Evidence for India; Presented at National Seminar on “ Role of Statistics in current Scenario” on 27th March 2016 organized by Department of Statistics, Veer Narmad South Gujarat University, Surat.
Monika Shah : “Some neglected aspects of conventional Shewhart control charts for variable ( with fixed location parameter) in SPC” ; One day state level seminar on “RECENT STATISTICAL TRENDS IN BUSINESS AND MANAGEMENT (RSTBM)” on 1st Feb-2015, organized by Dept. of Statistics, VNSGU, Surat and UCCC & SPBCBA & UACCAIT, Managed by Udhna Academy Education Trust, Udhna-Surat.
Monika Shah: “An empirical study on application of Wilcoxon signed rank test in Statistical Process control, when process target is fixed” National seminar on “ Role of Statistics in Current Scenario” on 27th March 2016,organized by Department of Statistics, Veer Narmad South Gujarat University, Surat.
Monika Shah: “Nonparametric control charts for scale parameter”, ICSSR Sponsored Two Days International Conference “ Emerging Issue In Development For Future Generation” during 12-13 February 2020, Jointly Organized by J. Z. Shah Arts and H. P. Desai Commerce College, Amroli & The International Institute For Development Studies (IIDS), Australia at J. Z. Shah Arts and H. P. Desai Commerce College, Amroli, Surat.
Shital S. Patel: “Statistical Quality Control of Multi-item inventory model with different demand and deterioration rates under shortages “ in 2nd International Conference (online mode) on “Mathematical Modelling and Simulation in Physical Sciences organized by Department of Mathematics and Humanities & Department of Physics, S. V. National Institute of Technology, Surat during 5-6 Feb. 2022
Shital S. Patel: “Study on Inventory models under Stochastic demand with unsteady deterioration rate “ in 2nd International Conference on Recent Trends in Mathematical Sciences (online mode) organized by HIMACHAL GANITA PARISHAD (HGP) Shimla during 27-28 Dec. 2022
Shital S. Patel: “An EPQ Model for Variable Production Rate With Exponential Demand and Probabilistic Deterioration “ in International Conference of the Society of Statistics, Computer and Applications(SSCA) ON Significance of Statistical Sciences organized by Department of Statistics, Jammu University, Jammu during 15-17 Feb. 2023
Shital S. Patel: “Impact of Probabilistic Demand on EOQ Models “ in 4th Prof. P. C. Vaidya International Conference on Mathematical Sciences organized by Department of Mathematics, VNSGU, Surat during 4-5 March 2023
Shital S. Patel: “Multi-item Probabilistic Inventory Model with Different Deterioration rates and Shortages via Lagrange method under capacity constraints “ in International Conference on Research Trends in Mathematics and Data Science organized by PG Department of Mathematics & Department of Computer Science with Data Science (online mode), Patrician College of Arts and Science, Chennai during 1-2 March 2024
“Inventory Management Modeling”; in One-day State Level Seminar on ‘Recent Statistical Trends in Business and Management’ on 1st February, 2015 at Udhna Citizen College, Udhna, Surat, Gujarat.
“Optimization Modeling” on 24.06.2015 during One Week Short Term Training Programme (STTP) on ‘Mathematical and Optimization Modeling with Simulation by Scientific Tools for Researchers, Engineers and Scientists (MOMSRES)’ at Department of Applied Mathematics and Humanities, SVNIT, Surat.
“Modeling and Analysis of Deteriorating Items Inventory Models” on 25.06.2015 during One Week Short Term Training Programme (STTP) on ‘Mathematical and Optimization Modeling with Simulation by Scientific Tools for Researchers, Engineers and Scientists (MOMSRES)’ at Department of Applied Mathematics and Humanities, SVNIT, Surat.
“Different Deterioration Rates of Two Warehouse Inventory Models” at International Conference on Gravational, Cosmology and Mathematical Physics organized by Department of Mathematics and Physics, GLA University, Mathura (04 to 07 April 2019)
“ Mathematical Modeling To Real World Problem” at TEQIP-II sponsored STTP on Mathematical Modelling and Simulation for Researchers, Engineers and Scientists (MMSFRES)organizes by Department of Applied Mathematics & Humanities, SVNIT, Surat ( 26-12-2019)
Dr. Arti J. Rajyaguru (Professor and Head)
Misuse of Statistics, Seven Day Workshop on Research Methodology in Social Sciences., Surat, 29-09-2017
Definition and nature of research-tools for thinking, Ethics in research, Computer: It's role in research, 7 days Short Term Course, Ahmedabad, 01-01-2018
Future careers with statistics, University guest lecture series, Surat, 31-01-2018
Career opportunities with statistics, Career Guidance program for students of 12th standard , Surat, 25-02-2018
Applications of Statistics for engineering and computer science, Expert Talks/Seminars during 2017-18, Surat, 12-09-2018
Using statistics correctly, University guest lecture series, Valsad, 12-09-2018
Sample size and Effect size, Recent advances in Statistics and statistical practices, V. V. Nagar, 02-03-2019
Applications of different statistical tests, GUJCOST, DST, Gov. of Gujarat sponsered, one day semianr on Recent trends in Applied mathematics and Statistics, Bardoli, 25-03-2019
Sample size, effect size and statistical / mathematical modeling, 2nd Prof. P. C. Vaidya National conference on Mathematical Sciences, Bhavnagar, 29-12-2019
Dr. Shital S. Patel (Assistant Professor)
Statistical Data Analysis: From Data Collection to Visualization” guest lecture in Workshop on Statistical Data Analysis: From Data Collection to Visualization organized by S. S. Agrawal Institute of Physiotherapy and Medical Care Education, Navsari on 24/06/2023
“Statistical Methods in Biotechnology: Practical Approaches” guest lecture in Department of Biotechnology, VNSGU, Surat on 04/09/2023
“Analyzing Statistical Data using Jamovi” guest lecture in workshop organized by SPB Physiotherapy College (MPT), Rander, Surat on 29/12/2023
Scholar Name: Mital C. Shingala Guide Name: Dr. A. J. Rajayguru Title: COMPARISON OF DIFFERENT POST HOC TESTS Status: Completed Year 2017
Scholar Name: Neha Sheth Guide Name: Dr. V. D. Naik Title: A COMPRENSIVE QUANTATIVE STUDY ON QUALITY OF LIFE OF BREAST CANCER PATIENTS Status: Completed Year 2017
Scholar Name: Sanjay D. Patel Guide Name: Dr. A. J. Rajayguru Title: DETERMINATION OF CONSUMPTION PATTERNS – A COMPARATIVE STUDY OF GUJARAT AND INDIA. Status: Completed Year 2018
Scholar Name: Shehnazparvin Riyaz Sheikh Guide Name: Dr. R. D. Patel Title: STUDY ON INVENTORY MODELS FOR DETERIORATING ITEMS WITH DIFFERENT DETERIORATION RATES Status: Completed Year 2018
Scholar Name: Tarulata Patel Guide Name: Dr. R. D. Patel Title: STUDY ON OPTIMALITY AND DUALITY FOR DIFFERENTIABLE AND NON DIFFERENTIABLE MULTI OBJECTIVE OPTIMIZATION PROBLEMS Status: Completed Year 2018
Scholar Name: Jayshree J Pandey Guide Name: Dr. R. D. Patel Title: INVENTORY MODELS FOR SUPPLY CHAIN WITH SINGLE VENDOR AND MULTIPLE BUYERS Status: Completed Year 2019
Scholar Name: Bhavana Naik Guide Name: Dr. R. D. Patel Title: STUDY ON DETERIORATING ITEMS INVENTORY MODELS WITH DIFFERENT DETERIORATION RATES FOR DEFECTIVE AND REPAIRABLE ITEMS Status: Completed Year 2020
Scholar Name: Jiten Patel Guide Name: Dr. R. D. Patel Title: SOME STUDIES ON INVENTORY MODELS FOR DETERIORATING ITMS UNDER SUPPLY CHAIN FOR DECISION MAKING Status: Completed Year 2020
Scholar Name: Monika Shah Guide Name: Dr. Sejal A. Desai Title: NON PARAMETRIC TESTS FOR STATISTICAL QUALITY CONTROL WHEN LOCATION PARAMETER OF PROCESS IS FIXED Status: Completed Year: 2023
Scholar Name: Kavita Rathod Guide Name: Dr. A. J. Rajayguru Status: Pursuing
Scholar Name: Anilkumar Panchal Guide Name: Dr. A. J. Rajayguru Status: Pursuing
Scholar Name: Pankajkumar Parmar Guide Name: Dr. Sanjay D. Patel Status: Pursuing
Scholar Name: Dishaben Bharodiya Guide Name: Dr. Sanjay D. Patel Status: Pursuing
Scholar Name: Varsha Rajput Guide Name: Dr. Shital S. Patel Status: Pursuing
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
M.Sc.(Statistics)
Master of Science (M. Sc.) (Statistics) program is designed for Statistics and Mathematics (Statistics as principal or Mathematics as principal subject and Statistics as subsidiary or both Mathematics and Statistics as optional subjects) graduate students. Therefore, the first semester courses are designed to bridge the gap between subjects studied at the graduate level. The curriculum is designed and updated time to time to match the industrial and academic requirements. It is two year grant in aid program with four semesters.
Syllabus Download
Brochure
Objective of Program
The core objective of the program is to prepare the students to be capable of doing every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (GIA) : 38
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
101
Core-I:Probability Theory
After completing this course, the students will be able to
CO1: The aim of the course is to pay a special attention to applications of Real Analysis in the foundation of probability theory. CO2: Students learn to identify the characteristics of different Discrete and continuous variables. CO3: The knowledge to define the type of variables for different situation to which different concepts of probability theory can be Applied. CO4: Understanding of the concept of expectation and conditional expectation and their real life applications. CO5: Students learn to develop and apply different moment inequalities for statistical inference purpose. CO6: Gain the ability to understand the concepts of random variable, Sequence of random variables, convergence, modes of convergences. CO7 : understanding of Weak Law of Large Theorem with their applications e.g. large-sample approximations for common statistics.
4
102
Core-II: Univariate Distributions
After completing this course, the students will be able to:
CO1: Understand the most common discrete and continuous probability distributions and their real life applications. CO2: Calculate moments, quartiles and characteristic function from distributions CO3: Get familiar with different transformation of univariate distribution CO4 :Apply compound, contagious, Neyman type-A and Truncated distributions to solve problems CO5:Aware about power series distributions CO6: Differentiate between central and non-central distributions CO7: On studying the theory of order statistics students can learn how to model product failure, droughts, floods and other extreme occurrences.
4
103
Core-III: Linear Algebra
After completing this course, the students will be able to:
CO1: Understanding and applying basic concepts of linear Algebra. CO2: Identifying applications of Matrix Algebra in statistics CO3:Express and solve system of equations with multiple dimensions/variables in matrix notations. CO4: Understand use of determinants, inverse of a matrix rank, characteristic polynomial, Eigen values, Eigen vectors etc. other special types of matrices. CO5: Understand concepts of linear transformation, linear product and quadratic equations with their applications
4
1041
Elective-I: Real Analysis
After completing this course, the students will be able to:
CO1:Describe fundamental properties of the real numbers, sets, classes, function, inverse function, simple and measurable functions, distribution functions, measures etc. that lead to the formal development of real analysis/ probability theory. CO2:Comprehend rigorous arguments developing the theory underpinning real analysis and base to probability theory. CO3: Demonstrate and understanding of limits of sequences, series etc.Construct rigorous mathematical proofs of basic results in real analysis. CO4: Students will be aware of the need and use of Real Analysis. CO5: Concept of measure, its properties, and important results related to measure & their proofs and Construction of Lebesgue measure and Lebesgue Stiltjes measure.
4
1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to:
CO1: Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
105
Practical Paper - I
After completing this course, the students will be able to: CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Understand and apply various functions available in excel and JAMOVI CO5: Fit the distributions to a real life data using Excel and JAMOVI CO6: Analyze real life data of various sampling techniques CO7: Solv linear algebra problems by excel CO8: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industries etc. CO9: Application of Real Analysis
4
106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
4
M. Sc. (Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
201
Core-I: Estimation Theory
After completing this course, the students will be able to:
CO1: Understand the concept of estimator with different properties. CO2: Demonstrate and understanding the concept of unbiasedness and basedness with theory CO3: Derive a foundation on different theorem based on estimators CO4: Describe the concept of BLUE, BAN, MVUE, MVBUE, UMVUE CO5: Students have the knowledge methods of obtaining minimum variance unbiased estimators CO6: Learn the methods for interval estimation for small and large sample size.
4
202
Core-II: Testing of Hypothesis
After completing this course, the students will be able to:
CO1: Formulate null and alternative hypothesis; understand types of errors involved in the testing of hypothesis, concepts for comparisons of different possible test procedures to decide the test for best test for various types of null and alternative hypothesis for different real-life situations. CO2: Compute probabilities of type of errors and checking MLR property CO3: Understand UMP and UMPU test with their applications and relevant results. CO4: Construct MP test, UMP test and UMPU test. Knowledge of SLRP & GLRT and SPRT. CO5: Use the concept and related results of invariant testing of hypothesis and their applications CO6: Construct best test for distributions, which are not well behaved CO7: Use concepts of least favorable distribution for testing of hypothesis.
4
203
Core-III: Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the development of multinomial and multivariate normal distribution with their properties. CO2: Understand the concept of Wishart distribution with various properties CO3: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO4: Get Derivation of Hotelling T2 statistic and their various application in real life problems CO5: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO6: Understand the concept of data reduction technique like factor, principal and Canonical correlation analysis
4
2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2042
Elective-II: Decision Theory
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to:
CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
2044
Elective-IV: Database Management System
After completing this course, students will be able to:
CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems. CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel. CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problems related to multivariate data with use of excel CO6: Apply parametric tests to solve real life problems using excel
6
206
Computer Programming Language -C
CO1: Handle and process the data using excel CO2: Perform the analysis with analysis tool pack in excel CO3: Understand and apply various functions available in excel. CO4: Estimate parameters using formula in excel by different methods CO5: Solve problem related multivariate data with use of excel CO6: Apply sampling technique to solve real life problem using excel
2
M. Sc. (Statistics) Sem III (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
301
Core-I: Non-Parametric Inference
After completing this course, the students will be able to:
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
302
Core-II: Linear Model
After completing this course, the students will be able to:
CO1: To understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO2: To understand the use and need of restricted linear regression and related theory CO3: To understand the process of simultaneous estimation of parametric functions, use of quadratic form, canonical form etc for different purposes. CO4: Cochran’s theorem and its application for linear models
4
303
Core-III: Sampling Theory -II
After completing this course, the students will be able to:
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3: Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
3041
Elective-I: Statistical Simulation
After completing this course, students will be able to:
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4:Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5:Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6:Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7:Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
3042
Elective-II: Data Mining
After completing this course, students will be able to:
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4:Describe the principles of clustering and its applications in unsupervised learning. CO5:Understand the principles of neural networks and their applications in optimization and function approximation. CO6:Apply genetic algorithms to solve optimization problems in various domains.
4
3043
Elective-III: Stochastic Process
After completing this course, students will be able to:
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: Describe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behavior CO4:Analyze Poisson processes and their applications in various fields CO5:Identify the characteristics of queuing systems and their parameters CO6:Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
305
Practical Paper - III
After completing this course, students will be able to:
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
306
Statistical Computing Using SPSS
After successful completion of this course, student will be able to:
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4:Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5:Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Statistics) Sem IV (As per NEP 2020 implemented from the academic year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research
CO1: Understand basic concepts and techniques of sensitivity analysis in linear programming with different cases CO2: Comprehend the fundamentals of integer programming and its type with implement of Gomory’s algorithm to solve IPP CO3: formulate goal programming problems to address multiple conflicting objectives in decision-making process CO4: Identify different types of replacement problems and apply appropriate replacement strategies. Utilize replacement theory concepts in real-life situations. CO5: Identify the characteristics and advantages of dynamic programming in solving optimization problems. CO6: Solve sequencing problems with various job-machine, task sequencing in project management and scheduling jobs on machines in manufacturing processes. CO7: Students should be able to apply optimization techniques to address complex decision-making problems across various domains, effectively managing resources, minimizing costs, and maximizing efficiency in real-life situations.
4
402
Core-II: Design Of Experiments
CO1: Understand the concept of design and conduct experiments efficiently and effectively CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
CO1: Get knowledge about formulating a linear model for the given situation CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures . CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
4041 : Elective-I: Biostatistics & Clinical Research
CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
4042 :Elective-II: Economics and Business Statistics
CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5 : Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
4043 : Elective-III: Project/ Dissertation
CO1. It will develop the research aptitude. CO2. Students will get training to work as team member/leader. CO3. It will improve their presentation, teamwork, leadership and communication skills. CO4. The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
CO1: Apply operations research techniques for optimization in business and real data. CO2:Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (STATISTICS)
(1) A Students is eligible for the M.Sc. (statistics) program under the Faculty of Science if Statistics/Applied Statistics/ Data Science/ Dada Analytics has been studied as a major/ principal, or Mathematics as a major/ principle subject and Statistics/ Applied Statistics/ Data Science/ Data Analytics as a minor/ subsidiary in the B.Sc. Program.
(2) Admission will be based on the student's performance in the B.Sc. Program. Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the UG program, a student is eligible for admission to the M.Sc. (Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided that there are available seats after the admissions based on the criteria in point (1). Admission will be based on the student's performance in the university-level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Grant in Aid (GIA) - *Fees per Semester
Regular
Higher Payment
SF
Male
Rs. 6935*/-per semester
--
Female
Rs. 4435*/- per semester
--
*Subject to Revision Periodically
Master Of Science (Applied Statistics)
Master of Science (M. Sc.) (Applied Statistics) program is specially designed for non science as well as science stream students who studied Statistics at UG level at least as a subsidiary subject. This program provides great opportunity to non science students to be a Data Scientist/Statistical Analyst/Research Analyst etc. In other words this program offers a golden opportunity to non science as well as science students for building up their career in field of Statistics. The first semester courses is so designed as to bridge the gap of basic knowledge of Mathematics, Statistics and Basics of Computer. The curriculum is designed and updated time to time to match the industrial and academic requirements.
Syllabus Download
Brochre
Objective of Program
The core objective of the programme is to prepare the students to be capable of doing any kind and every kind of data analysis and to be helpful to the society and academia by providing an outstanding environment of teaching and research in the core and emerging areas of the discipline.
Program Outcome
PO1 : Fundamental Knowledge Enrichment Program trains students with the core statistics knowledge. It also makes students capable of using core concepts in the conceptualization of domain specific application development. PO2 : Critical Thinking Development The program develops the skills of critical thinking, problem solving, evaluative learning of various techniques, and understanding the essence of the problem. PO3 : Advanced Emerging Technology Awareness The program trains students with the latest technologies that are being used in the industry/ research. The continuous syllabi review adds value to the program for the outgoing students and make them ready to face challenging demands of the industry. PO4 : Advanced Tools Usage The program teaches the students to apply the advanced tools to solve real world problems. PO5 : Nurturing Project Planning and Management Capabilities The program trains students for designing and conceptualizing the statistical techniques and software architecture, planning and managing the process of complex real life problems in statistical frame work. It also makes students understand the decision making for an appropriate technique selection capability. PO6 : Real World Problem / Project Development Real world project provides the candidates exposure to work in the challenging and demanding environment of the industry/research. The project development training makes students employable and industry ready. PO7 : Team Work and Leadership Development Trains students to work in a team and also to take leadership of the of the project management team.
Intake
Grant in Aid (Higher Payment) : 50
Program Duration
2 years
Program Features
96 credit program ( 4 semester of 24 credits each)
Total 4 courses covering various technologies.
4 practical courses
Practical exposure for the applications of different statistical techniques as a part of project in last semester.
The curriculum is designed and updated time to time to match the industrial and academic requirements.
M. Sc. (Applied Statistics) Sem I (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS- 101
Core-I: Basic Mathematics and Elements of Probability Theory
After completing this course, the students will be able to: CO1: Understand the concept of functions, Differentiation and Integration with application. CO2: Understand some standard series of positive terms. Concept of interpolations and its application. CO3: Understand the concept of determinant and matrices. Types of matrices and its application. CO4: Understand the concept of Permutation and Combination with some examples. CO5: Understand the concept of Probability and its applications CO6: Understand the use of discrete and continuous probability distributions, including requirements, mean and variance, and making decisions. CO7: Identify the characteristics of different discrete and continuous distributions. CO8: Identify the type of statistical situation to which different distributions can be applied. CO9: Understand the most common discrete and continuous probability distributions and their real life applications. CO10: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distribution. CO11: Understand distribution which will help to understand the nature of data and to perform appropriate analysis.
4
MAS-102
Core-II: Probability Distributions
After completing this course, the students will be able to: CO1: Understand the use of discrete and continuous probability distributions, including requirements, properties of distributions and its use in making decisions. CO2: Identify the characteristics of different discrete and continuous distributions. CO3: Identify the type of situation to which different distributions can be applied. CO4:Understand the most common discrete and continuous probability distributions and their real life applications CO5: Compute marginal and conditional distributions from joint distributions. Get familiar with transformation of univariate and bivariate distributions. CO6: Understand the distribution which helps to understand the nature of data and selection of appropriate analysis.
4
MAS-103
Core-III: Operations Research I
After completing this course, the students will be able to: CO1: Identify situations in which LP technique can be applied. CO2: Formulate and solve linear programming problems, using graphical method, simplex, two-phase and Big-M method. CO3: Understand the concept of duality, their properties, relationship between primal-dual and LP problems. CO4: Realize the need to study replacement and maintenance analysis techniques and make distinctions among various types of failures. CO5: Aware about transportation problem with their properties, methods and real life applications. CO6: Understand the features of assignment problems with transportation problems & apply proper method to solve an assignment problem. CO7: Understand the meaning of inventory control s well as various forms and functional role of inventory with EOQ model with different scenario like probabilistic and deterministic situations. CO8: Understand how optimal strategies are formulated in conflict and competitive environment.
4
MAS-1041
Elective-I: Population Studies
After completing this course, the students will be able to: CO1: Apply demographic concepts and population theories to explain past and present population characteristic. CO2: Comprehend the basic components of population (fertility, mortality, migration) CO3: Study established theories of population. CO4: Get a better understanding of the current demographic profile of India. CO5: Acquire skills to use life tables and calculate survival rates CO6: Be familiarize with the methods of Population projection.
4
MAS-1042
Elective-II: Sampling Theory I
After completing this course, the students will be able to: CO1:Understand the basic principles of survey design, sample size determination, estimation. CO2: planning and execution of different types of survey and Report writing of a survey. CO3: Apply different sampling methods, like SRSWR, SRSWOR, post/ PRE-stratification (stratified sampling), and selecting a sample from population under study and estimation of parameter of interest
4
MAS-1043
Elective-III: Official Statistics
After completing this course, the students will be able to: CO1: Understand Indian Statistical Systems, its role, functions and activities i.e. understand the role of MOSPI, NSO, and National Stastical Commission. CO2: Appreciate and use techniques of quantitative analysis in social work research . CO3: Discuss the scope and contents of population census of India. CO4: Identify Statistics related to agriculture, industries, health, prices, cost of living inflation, educational and social statistics etc. CO5: Understand economic development and national income estimation CO6: Discuss the measures of inequality in income and measures of incidence and intensity.
4
MAS-105
Practical Paper - I
CO1: Solve the real life problems of various basic mathematics and process the data using excel and JAMOVI CO2: Develop thinking skills of calculus. CO3: Calculate the probability, expected value and the moments. CO4: Fit the distributions to a real life data using Excel and JAMOVI CO5: Analyze real life data of various sampling technique CO6: Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic. CO7: Solve the real life problems of different variable and attributes chars using excel/JAMOVI CO8: Identify the different components of the Excel worksheet CO9: Construct formulas to manipulate numeric data in an Excel worksheet and understanding functions of JAMOVI CO10: Access and manipulate data using the database functions of Excel and performing practicals using JAMOVI CO11: Analysis and interpretation of official statistics, practical use in various sectors: Price, Agriculture, Industry etc.
6
MAS-106
Statistical Computing with Excel and Jamovi
After completing this course, the students will be able to: CO1: Understand and prepare various types of documentation and apply formatting using word processing application software CO2: Understand and prepare various types of documentation and apply formatting features using word processing application software CO3: Expertise in preparation of presentation by power point tool and its features CO4: Known various functions of excel for arranging data CO5: Develop statistical skills using JAMOVI software CO6: Develop understanding of browser, server, various internet protocols and utilities. CO7: Able to develop basic website designing using dream weaver.
2
M. Sc. (Applied Statistics) Sem II (As per NEP 2020 implemented from the academic year 2023-24) ▾
Course Code
Course Title
Outcome
Credit
MAS-201
Core-I: Statistical Inference I
After completing this course, the students will be able to: CO1: Understand the concept of estimator with different properties CO2: Demonstrate and understanding the concept of unbiasedness and biasedness CO3: Become aware of statements of different theorem based on estimators and applies it in suitable situations. CO4: Describe the concept of BAN, MVUE, MVBUE, and UMVUE. CO5: Have the knowledge of methods of obtaining minimum variance unbiased estimators. CO6: Learn the methods for interval estimation for small and large size confidence internal
4
MAS-202
Core-II: Statistical Inference II
After completing this course, the students will be able to: CO1: Get the knowledge about formulating the hypotheses, deciding appropriate test for concern parameters of interest and testing a hypothesis, using critical values to draw conclusions and determining probability of errors in hypotheses tests. CO2:Get the knowledge about large sample and small tests and its applications CO3: Get knowledge about classical testing of hypotheses testing and sequential testing of hypotheses testing. CO4: Understand the difference between classical and sequential testing of hypotheses. CO5: Compare two classical tests as well as sequential tests. CO6: Understand the situation for applying suitable test.
4
MAS-203
Core-III: Applied Mulitvariate Analysis
After completing this course, the students will be able to: CO1: Understand the concept of multinomial and multivariate normal distribution with their properties. CO2: Understand the idea of partial and multiple correlation coefficient with testing of hypothesis CO3: Demonstrate Hotelling T2 statistic and their various application in real life problems CO4: Demonstrate the knowledge and understanding of the basic ideas behind classification and discriminant analysis CO5: Understand concept of data reduction technique like factor analysis and principal component
4
MAS-2041
Elective-I: Industrial Statistics
After completing this course, students will be able to:
CO1: Understand the basics of production process monitoring and apply concept of control charts on it. CO2: Apply the acceptance and continuous sampling plans in production process. CO3: Know and apply the concept of weighted control charts, sixsigma,ISO:9000 series standards and Taguchi design. CO4:Understand the concepts of quality control, chance and assignable causes of variation,control charts for variables and attributes, producer’s and consumer’s risk- Acceptance sampling plans. CO5:Get idea of important life time distributions such as for exponential, Weibull, gamma and lognormal distributions. CO6:Use of estimation in their liability analysis.
4
MAS-2042
Elective-II: Decision Theory
After successful completion of this course, student will be able to: CO1: Identify and deal with the situations of decision making under risk and uncertainty CO2: Understand decision problem, loss function, risk function and decision rules, their admissibility and completeness CO3:Use of different decision rules under uncertainty and risk. CO4: Obtaining best decision rules using different types of prior, posterior distributions and loss functions
4
MAS-2043
Elective-III: Actuarial Statistics
After completing this course, students will be able to: CO1: Understand the utility theory, insurance products and life tables. CO2: Understand the concept to of interest. CO3: Understand the concept of life insurance and the existing insurance products of different insurance company. CO4:Know life annuities, net premium and net premium reserves. CO5:Understand the concept of Stationary population and various Models
4
MAS-2044
Elective-IV: Database Management System
After completing this course, students will be able to: CO1: Understand and apply the concept of data base management system by comparing them with traditional data management techniques CO2: Perform data definition, data manipulation, data control and transaction control using Query language CO3: Learn fundamental data models and its application in real world domain. CO4: Extend the procedural structural query language using various Concept such as Procedures, Functions, Cursor and Triggers Mapping between COs
4
MAS-205
Practical Paper - II
After successful completion of this course, student will be able to:
CO1: Solve Decision related real life problems using decision criteria, can solve life-table related problems.
CO2: Perform the analysis with analysis tool pack in excel and generate/draw Quality control charts in excel.
CO3: Understand and apply various functions available in excel.
CO4: Estimate parameters using formula in excel by different methods
CO5: Solve problem related multivariate data with use of excel
CO6: Apply parametric tests to solve real life problem using excel .
6
MAS-206
Computer Programming Language -C
After completing this course ,students will be able to: CO1: Understand the basic concepts and fundamentals of programming such as algorithm and flowchart. CO2: Understand the basic C fundamentals such as data types, operator set c. CO3: Design programs involving control statements such as conditional and unconditional statements. CO4: Implement advanced programming approach such as modular programming along with parameter passing techniques. CO5: Understand the concept of different data structures such as array, structure and union. CO6:Develop the programs that deal with various operations on data files.
2
M. Sc. (Applied Statistics) Semester III ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
MAS-301
Core-I: Statistical Inference -III
CO1: Get knowledge to discriminate the difference between parametric tests and Nonparametric tests. CO2: Get knowledge to decide appropriate non parametric test for the given situation/data. Understand the logic for applying suitable Nonparametric tests. CO3: Get knowledge about different nonparametric tests and its application in real life problem. CO4: Get knowledge about different test when size of the sample is very small for One sample tests, two sample tests and more than two samples tests.
4
MAS-302
Core-II: Applied Regression Analysis
CO1: Understand the fundamental concepts underlying regression analysis, including assumptions, model building, interpretation of coefficients, and model diagnostics. CO2: Understanding general linear models under Gauss-Mark off set up (GMLM and GGMLM) including parameter estimation using method of least squares and its significance testing CO3: Understand the use and need of restricted linear regression and related theory CO4: Understand the use and need of restricted linear regression and related theory CO5: Understand the need of count data regression.
4
MAS-303
Core-III: Sampling Theory II
CO1: Understand systematic random sampling, cluster sampling, two stage, multi stage sampling, two phase, and PPS sampling techniques, including their principles, advantages, and limitations. CO2: Do estimation using ratio method of estimation, product method of estimation and regression method of estimation, to derive best estimates of population parameters from the sample data. CO3:Identify and analyse sources of error in surveys, including sampling errors and biases, and apply appropriate methods to minimize their impact on survey results. CO4: Select appropriate sampling methods and implementing estimation techniques to produce reliable and valid survey results that can be used in decision-making processes in diverse fields.
4
MAS-3041
Elective-I: Statistical Simulation
CO1: Understand the principles of statistical simulations and their applications in generating random variables and gain proficiency in simulating various probability distributions. CO2: Compare and evaluate different algorithms for generating random variables, considering the efficiency and accuracy. CO3: Develop a deep understanding of multivariate distributions and their simulation techniques under (MCMC) methods and Gibbs sampling. CO4: Acquire knowledge of simulating non-homogeneous Poisson processes and their applications. CO5: Learn optimization techniques and develop skills in solving differential equations through Monte Carlo methods CO6: Understand the concepts of re-sampling methods including jackknife and bootstrap. CO7: Develop critical thinking skills to evaluate the validity and reliability of statistical inference using resampling methods.
4
MAS-3042
Elective-II: Data Mining
CO1: Understand the fundamental concepts of data mining and its applications in various domains. CO2: Apply data preprocessing techniques to clean, transform, and prepare data for analysis. CO3: Explain the principles and applications of classification algorithms in supervised learning. CO4: Describe the principles of clustering and its applications in unsupervised learning. CO5: Understand the principles of neural networks and their applications in optimization and function approximation. CO6: Apply genetic algorithms to solve optimization problems in various domains.
4
MAS-3043
Elective-III: Stochastic Process
CO1: Understand the concept of stochastic processes and their applications in modelling CO2: CDescribe stationary processes and their significance in various real-world scenarios CO3: Classify states of a Markov chain and discuss their implications for system behaviour CO4:Analyze Poisson processes and their applications in various fields CO5: Identify the characteristics of queuing systems and their parameters CO6: Analyze birth, death, and birth-death models and understand their applications in various queuing systems CO7:Understand the M/M/1 and M/M/C queuing model and its components
4
MAS-305
Practical Paper - III
CO1: Develop an understanding of parametric inference methods, including estimation and hypothesis testing. Learn how to apply these techniques to analyze data and draw conclusions about underlying distributions. CO2: Understand how to build, interpret, and validate these models to address real-world data analysis challenges. CO3: Learn how to design sampling strategies for accurate data collection and ensure the validity of statistical analysis. CO4: Learn the principles and practices of statistical simulation, including Monte Carlo methods. Gain experience in using simulations to model complex processes, estimate probabilities, and validate statistical results. CO5: Explore the basics of data mining and machine learning. Understand how to apply techniques such as clustering, classification, and association rules to discover patterns and insights from large datasets. CO6: Gain hands-on experience in data collection, cleaning, analysis, and reporting, enhancing practical skills and industry readiness.
6
MAS-306
Statistical Computing Using SPSS
CO1: Students should gain a solid understanding of fundamental statistical concepts CO2: Students should become proficient in using SPSS software for data analysis. CO3: Students should learn how to manage data effectively within the SPSS environment. CO4: Students should learn a variety of statistical techniques commonly used in research, such as t-tests, ANOVA, chi-square tests, correlation analysis, and regression analysis, and how to perform these analyses using SPSS. CO5: Students should develop the ability to interpret the results of statistical analyses performed in SPSS CO6: Students should be able to apply their knowledge of statistical computing with SPSS to research projects or practical situations in various fields CO7:Completion of the course should prepare students for further study in fields that require statistical analysis skills or for employment opportunities
2
M. Sc. (Applied Statistics) Semester IV ( As Per NEP 2020 Implemented From The Academic Year 2024-25) ▾
Course Code
Course Title
Outcome
Credit
401
Core-I: Operations Research II
After completing this course, the students will be able to: CO1: Understand basic concept of sensitivity analysis with changes in objective function, vector b and matrix A. Also discuss the cases for addition and deletion of variable and constrains with example CO2: Construct integer programming problems with different types to discuss the solution techniques. CO3: Apply integer programming problem in practical situations. CO4: Understand the concept of PERT/CPM and their real life application CO5: Select the best sequence through different machine to different jobs to minimize time. CO6: Develop the concepts of dynamic programming and their applications.
4
402
Core-II: Applied Design of Experiments
After completing this course, the students will be able to: CO1: Understand the concept of design and conduct experiments efficiently and effectively. CO2: Analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. CO3: Develop the concept of various designs CRD, RBD, LSD, BIBD, Factorial and Confounded designs. CO4: Understand the difference between various designs of experiments. CO5: Particular attention will be paid to understanding the process of designing an experiment including factorial and confounding designs.
4
403
Core-III: Econometrics
After completing this course, the students will be able to: CO1: Get knowledge about formulating a linear model for the given situation. CO2: Get knowledge about different types of possible problems with data, their identification, confirmation, consequences as well as respective remedial measures. CO3: Get knowledge about selection of different types of models for different types to data and situation of real-life problems. CO4: Get knowledge to discriminate between single equation models and SEM. CO5: Get knowledge of formulation, estimation and prediction with SEM.
4
4041
Elective-I: BioStatistics & Clinical Research
After completing this course, students will be able to: CO1: Plan study design and do data analysis for the health / biological/medical sciences. CO2: Understand how the basic principles of probability are useful for biostatistics. CO3: Do survival Analysis/ failure analysis for the health /biological/medical sciences. CO4: Estimating the risk of one type of failure after removing others CO5: Evaluate, from simple datasets, evidence for linkage disequilibrium and disease associations using basic association tests CO6: Planning, analyzing and interpreting statistical out puts of different phases of clinical trials, including deciding sample size.
4
4042
Elective-II: Economic & Business Statistics
After successful completion of this course, student will be able to: CO1: Understand the concept of index numbers with their property CO2: Aware different types of index numbers useful in real life. CO3: Understand the purpose of demand analysis for different types of data and method for studying this. CO4: Understand the concept of time series analysis with components and its methods. CO5: Derive different time series models using Box-Jenkins methodology, remove trend and seasonality using different methods to convert the time series into stationary. CO6: Check and validate models with its residual analysis and diagnostic checking.
4
4043
Elective-III: Project/Dissertation
After completing this course, students will be able to:
CO1: It will develop the research aptitude. CO2: Students will get training to work as team member/leader. CO3: It will improve their presentation, teamwork, leadership and communication skills. CO4:The students will develop all the skill to be play a key role in every kind of research and development activity.
4
405
Practical Paper - IV
After successful completion of this course, student will be able to: CO1: Apply operations research techniques for optimization in business and real data. CO2: Design, conduct, and analyze experiments with advanced statistical methods to improve outcomes. CO3: Use econometric models to analyze and forecast economic trends for policy and business insights. CO4: Implement biostatistical methods to design clinical trials and interpret healthcare data. CO5: Utilize economic and business statistics to drive strategic decisions and assess financial performance. CO6: Manage and execute a comprehensive project, demonstrating data analysis and project management skills.
6
406
Programming Language ‘R’
After completing this course ,students will be able to: CO1: Understand basics of R environment. CO2: Perform various operations on data in R. CO3: Do descriptive statistical analysis in R. CO4: Perform different computational facilities provided in the package
2
Eligibility Criteria
MASTER OF SCIENCE (APPLIED STATISTICS)
(1) Students who have studied Statistics/ Applied Statistics/ Data Science/ Data Analytics as either a major/ principle or minor/ subsidiary subject in their undergraduate program are eligible for the M.Sc. (Applied Statistics) program under the Faculty of Science. Admission will be based on the student's performance in the undergraduate program.
(2) Regardless of the major/ principal or minor/ subsidiary subject/ discipline chosen in the Undergraduate program, students are eligible for admission to the M.Sc. (Applied Statistics) program under the Faculty of Science if they qualify the university-level entrance examination, provided there are available seats after the admissions under the criteria outlined in (1). Admission will be based on the student's performance in the university level entrance examination.
Admission Details
Merit Based following rules and regulations of government of Gujarat and Veer Narmad South Gujarat University. (Reservation will be based on the Reservation Policy by Government of Gujarat)
Intake: 50
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
Regular
Higher Payment
SF
Male
Rs. 13935*/- per semester
Female
Rs. 13935*/- per semester
*Subject to Revision Periodically
Ph.D.(Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
M.Sc (Statistics)
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Ph.D.(Applied Statistics)
Syllabus Download
Objective of Program
Ph.D. Programme in Applied Statistics is aimed towards promoting good research useful to the society through knowledge of Statistics. It will trained researcher for critical thinking and finding solution through Statistics. The researcher will be able to various types of research projects in the benefit of society. The Department offers Ph.D. programme in regular and part time modes. Ph. D. (Applied Statistics) offers and interdisciplinary exposure to the research students.
Intake
Depends on availablity of the supervisor
Eligibility Criteria
Post Graduate Degree in Applied Statistics braches
Admission Details
Entrance Test
Reservation Policy
Reservation will be based on the Reservation Policy by Government of Gujarat.
Fee Structure *
As per the University norms
*Subject to Revision Periodically
Certificate Course on PYTHON FOR STATISTICS
This course is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will study data design, data management, and how to effectively carry out data exploration and visualization. Learners will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. This course is designed by one of our alumnae Ms. Smita Shah having more than 35 years of experience as a Statistician. The faculties have more than 15 years of experience in teaching.
Syllabus Download
Objective of Program
Python is a popular general– purpose programming language that is well suited to a wide range of problems. The objective of this course is to get comfortable with the main elements of Python programming used for Statistical Analysis.
what you will learn
Learning Jupyter note book and Spyder.
Installing and understanding various basic libraries like Numpy, Pandas, Statsmodel, Matplotlib and Seaborn, Sklearn.
Descriptives Statistics and Visualization of data.
Inferential Statistical Analysis like ANOVA, Correlation and Regression, Parametric and NonParametric tests, etc.
Fitting Statistical model and Evaluation of the model.
Intake
60
Course Duration
45 Hrs.
View Brocher
Eligibility Criteria
No prior coding experience is necessary. Any candidate who has already passed H.S.C. with English as a compulsory subject and has a basic knowledge of Statistics is eligible for the course.
Fee Structure *
Course Fees
Rs.3000/-
*Subject to Revision Periodically
Certificate Course on Communicative English for Career(CEC)
Syllabus Download
Objective of Program
To enable the learner to communicate effectively and appropriately in real life situation.
Intake
60
Course Duration
35 hours
Eligibility Criteria
Any students can join after 10 th /12 th class
Fee Structure *
Course Fees
Rs.1600/-
*Subject to Revision Periodically
Certificate Course on Advance Excel for Business Analytics
Syllabus Download
Objective of Program
Looking to the needs ofsurrounding areas of south Gujarat region regarding knowledge of advanced excel analysis, the course is design to fulfill their requirements.
Intake
60
Course Duration
45 Hrs.
Eligibility Criteria
Minimum HSC
Fee Structure *
Course Fees
2700/-
*Subject to Revision Periodically
Certificate Course on Statistical Data Analysis using SPSS
Syllabus Download
Brochre
Objective of Program
1. Using SPSS software for data analysis. 2. To enhance the participant’s skills in presenting and visualizing data using SPSS. 3. To provide practical experience in applying statistical techniques using real-life datasets.
Intake
30
Course Duration
45 Hrs.
Eligibility Criteria
Any graduate having English as a compulsory subject and has basic knowledge of Statistics
Fee Structure *
Course Fees
Rs.3600/-
*Subject to Revision Periodically
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VNSGU
Veer Narmad South Gujarat University
The Registrar, Veer Narmad South Gujarat University Post Box No 49, Udhna Magdalla Road Surat – 395007, Gujarat, [INDIA]
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