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Master of Science (M.Sc.) Statistics Syllabus 2026: Subjects, Specializations, and Semester-wise Syllabus

  • course years 2 Years
  • type of course Post Graduate
  • course stream Science
  • course type Full Time
Written By universitykart team | Last Updated date Mar, 20, 2026

The M.Sc. Statistics curriculum covers advanced statistical theory, probability, data analysis, and mathematical modeling. Students delve into areas like multivariate analysis, regression analysis, Bayesian statistics, and experimental design. Practical skills in statistical software and data

Syllabus for M.Sc. in Statistics Course

The Master of Science (M.Sc.) in Statistics program offers a comprehensive curriculum designed to provide students with advanced knowledge and skills in statistical theory, methods, and applications. The coursework covers a wide range of topics, including probability theory, mathematical statistics, regression analysis, experimental design, multivariate analysis, time series analysis, and statistical computing using software like R or SAS. Students are also exposed to various domains where statistics plays a crucial role, such as biostatistics, financial statistics, and data science. Throughout the program, students engage in hands-on data analysis and research projects, enabling them to apply statistical methods to real-world problems and gain practical experience. Graduates of this program are well-prepared for careers as statisticians, data analysts, research scientists, and consultants in various industries, including healthcare, finance, market research, and government agencies

Syllabus of MSc. Statistics

Year 1

S.No
Subjects
1
Analysis 
2
Probability Theory
3
Statistical Methodology                                                                      
4
Survey Sampling
5
Linear Algebra
6
Stochastic Processes
7
Design of Experiments

Year 2

S.No
Subjects
1
Multivariate Analysis 
2
Generalized Linear Models                                                                
3
Econometrics and Time Series Analysis
4
Demography, Statistical Quality 
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