B.Stat (Hons)
B.Stat (Hons) Syllabus 2026
The B.Stat (Hons) (B.Stat (Hons)) syllabus covers a structured programme spanning 3 Years designed to build both foundational knowledge and specialised expertise. Below is the detailed semester-wise subject breakdown and programme structure.
B.Stat (Hons) Semester-wise Subjects
B.Stat (Hons) Syllabus & Subjects
The B.Stat curriculum consists of 30 one-semester credit courses (5 per semester) across 6 semesters. Each course has 3 lecture sessions + 1 practical/tutorial session per week. The programme combines deep statistical theory with mathematical foundations and computational skills.
Core Subjects
| Category | Key Subjects |
|---|---|
| Probability & Statistics | Probability Theory I & II, Statistical Methods I & II, Statistical Decision Theory & Inference, Large Sample Methods, Multivariate Analysis, Time-Series Analysis |
| Mathematics | Calculus I & II, Real Analysis, Vectors & Matrices I & II, Applied Stochastic Processes |
| Applied Statistics | Regression Techniques, Sample Surveys, Design of Experiments, Statistical Quality Control, Demography |
| Computing | Computational Techniques & Programming I & II (C, R, Python) |
| Electives | Natural Sciences (Physics, Chemistry, Biology) or Social Sciences (Economics, Sociology) |
B.Stat (Hons) Programme Structure & Credit Distribution
B.Stat (Hons) Year-Wise Curriculum
| Year / Semester | Courses (5 per semester) |
|---|---|
| Year 1 - Sem I | Calculus I, Probability Theory I, Vectors & Matrices I, Statistical Methods I, Computational Techniques & Programming I, Remedial English (non-credit) |
| Year 1 - Sem II | Calculus II, Probability Theory II, Vectors & Matrices II, Statistical Methods II, Computational Techniques & Programming II |
| Year 2 - Sem III | Real Analysis, Regression Techniques, Multivariate Statistical Analysis, Statistical Decision Theory & Inference, Elective |
| Year 2 - Sem IV | Applied Stochastic Processes, Large Sample Statistical Methods (Parametric & Nonparametric), Time-Series Analysis, Electives |
| Year 3 - Sem V | Statistical Quality Control, Design of Experiments, Sample Surveys, Demography, Elective |
| Year 3 - Sem VI | Advanced electives from statistics, natural sciences, or social sciences |
Programme Structure
- Total Courses: 30 one-semester credit courses across 6 semesters
- Teaching Load: 3 lectures + 1 practical/tutorial per week per course
- Elective Groups: Natural Sciences (Physics, Chemistry, Biology) and Social Sciences (Economics, Sociology)
- Progression: B.Stat graduates receive direct admission to M.Stat without entrance test
Skills Developed in B.Stat (Hons)
Skills Required & Acquired in B.Stat (Hons)
Skills Required for Admission
Comfort with advanced algebra, calculus, combinatorics, and geometry at a level significantly beyond 10+2 syllabus. Olympiad-level preparation is highly beneficial.
Ability to construct mathematical proofs, work with abstract concepts, and solve non-routine problems. The ISI entrance is proof-heavy.
ISI problems require creative approaches, not just formula application. Students who enjoy mathematical puzzles and competitions thrive.
The UGB paper requires sustained concentration on 8-10 proof-based problems. Comfort with open-ended mathematical exploration is essential.
Skills Acquired During B.Stat
Rigorous probability theory, statistical inference, decision theory, large sample methods, and multivariate analysis at a depth unmatched by any other UG programme.
Real analysis, linear algebra, stochastic processes - building the theoretical base for advanced statistical and data science work.
Programming in R, Python, and C for statistical analysis, simulation, and data processing. Computational techniques applied to real datasets.
Regression, time-series analysis, experimental design, sample surveys, quality control, and demography - practical tools for industry and research.
Building statistical models, interpreting data, and drawing inferences - the core competency demanded in data science and quantitative finance.
Rigorous logical reasoning and mathematical communication - essential for academic research and advanced problem-solving.