*Year 1: Foundations of Statistics & Mathematics* 

*Semester 1:* 

- *Descriptive Statistics* (Measures of Central Tendency, Dispersion) 

- *Probability Theory* (Basic Concepts, Bayes’ Theorem) 

- *Calculus* (Limits, Differentiation, Integration) 

- Lab: R/Python Programming Basics 

*Semester 2:* 

- *Probability Distributions* (Binomial, Poisson, Normal) 

- *Linear Algebra* (Matrices, Vector Spaces) 

- *Mathematical Statistics* (Sampling Distributions) 

- Lab: Data Visualization (ggplot2, Matplotlib) 

*Year 2: Core Statistical Methods* 

*Semester 3:* 

- *Statistical Inference* (Estimation, Hypothesis Testing) 

- *Regression Analysis* (Simple & Multiple) 

- *Design of Experiments* (ANOVA, CRD) 

- Lab: SPSS/Excel for Statistical Tests 

*Semester 4:* 

- *Multivariate Analysis* (PCA, Factor Analysis) 

- *Time Series Analysis* (Forecasting, ARIMA) 

- *Stochastic Processes* (Markov Chains) 

- Lab: Time Series Modelling (R/Python) 

*Year 3: Advanced Topics & Applications* 

*Semester 5: * 

- *Bayesian Statistics* 

- *Machine Learning Basics* (Clustering, Classification) 

- *Electives (Choose 1): * 

  - Actuarial Statistics 

  - Biostatistics 

  - Econometrics 

- Lab: Mini-Project (Real-world Dataset Analysis) 

*Semester 6: * 

- *Operations Research* (Linear Programming) 

- *Big Data Analytics* (Hadoop/Spark Basics) 

- *Capstone Project* (Industry/Research Problem) 

*Key Features: * 

- *Software Proficiency: * R, Python, SPSS, Excel. 

- *Interdisciplinary Links: * Economics (Econometrics), Biology (Biostats), AI (ML). 

- *Labs (30% weightage): * Hands-on data analysis, report writing.