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MSc. Statistics and Data Science
Program Details
The MSc. Statistics and Data Science is designed for the digital era, where organisations must extract meaningful insight from complex, high-dimensional data across sectors. It brings together statistics, mathematics, computation and AI as a unifying, transdisciplinary toolkit for real-world problem-solving. The program builds strong mathematical, statistical and computational foundations, enabling students to design models, analyse large-scale systems and make data-led decisions across domains.
Key components include grounding in data science, mathematics, statistics and computational science; domain-focused analytics (marketing, finance, climate and health); AI/ML (including reinforcement and deep learning); programming in MATLAB, Python and R; data visualisation and regression diagnostics; modelling, optimisation and simulation; and industry-oriented projects, internships and dissertation work.
Program Highlights
- Rigorous blend of theory and hands-on practice, focused on solving real-world problems with statistical and computational methods.
- Transdisciplinary curriculum spanning statistical sciences, data science, AI and computer science.
- Hands-on training using real datasets from satellite imagery, aviation, finance, environmental studies, e-commerce, healthcare and social sciences.
- Project-oriented pedagogy aimed at strong industry readiness.
- Expert talks and academic engagement with professors and researchers from leading institutions (IITs, NITs, IISERs, IIMs and IISc).
Industry Trends & Career Opportunities
As data volumes grow and AI adoption accelerates, demand is rising for professionals who can combine statistical reasoning with machine learning, optimisation and domain understanding. Graduates can explore opportunities across research and academia, government and the public sector, enterprise analytics, and innovation-led roles in start-ups. The program is well-suited to careers where decisions must be backed by robust modelling, uncertainty handling and evidence-based inference.
Placements
Typical roles include:
- Data scientist / machine learning engineer
- Business and marketing analyst
- Quant analyst / financial analyst
- Data engineer / big data specialist
- AI / deep learning specialist
- Statistician
- Academician / research roles
Fee Structure
Click here for detailed Fee Structure.
Curriculum
The overall structure of the program is coherent and aligned with national and global standards in statistics and data science. Detailed credit framework of the program is as below:
Program Duration: 2 Years (4 Semesters)
Total Credits: 87
Structure: Core Courses + Electives + Projects
Pedagogy: Lectures, Labs, Projects, Seminars
Semester 1
| Course Title | L | T | P | C |
|---|---|---|---|---|
| Statistical Programming: R / Python | 2 | 0 | 2 | 4 |
| Mathematics for Data Science | 4 | 0 | 0 | 4 |
| Databases and Data Visualization | 3 | 0 | 1 | 4 |
| Probability, Entropy & Monte Carlo Simulation | 3 | 0 | 1 | 4 |
| Computational Linear Algebra & Learning from Data | 3 | 0 | 0 | 3 |
| Data Analysis, Sampling and Regression | 4 | 0 | 0 | 4 |
Semester 2
| Course Title | L | T | P | C |
|---|---|---|---|---|
| Multivariate Analysis and Statistical Inference | 4 | 0 | 0 | 4 |
| Algorithms and Data Structures | 3 | 0 | 0 | 3 |
| Network Science, Social Analytics and OSINT | 3 | 0 | 0 | 3 |
| Elements of OR, Optimization, and Optimal Control | 4 | 0 | 0 | 4 |
| Data Mining and Machine Learning | 4 | 0 | 0 | 4 |
| Elective-I | 3 | 0 | 0 | 3 |
Semester 3
| Course Title | L | T | P | C |
|---|---|---|---|---|
| Bayesian Inference | 3 | 0 | 0 | 3 |
| Data Engineering | 3 | 0 | 1 | 4 |
| Stochastic Process, Markov Chain, and SDE | 3 | 0 | 0 | 3 |
| Deep and Reinforcement Learning | 3 | 0 | 1 | 4 |
| Elective-II | 3 | 0 | 0 | 3 |
| Research Methodology, Ethics, and Communication | 2 | 0 | 0 | 2 |
| Project – I | 0 | 0 | 0 | 4 |
Semester 4
| Course Title | L | T | P | C |
|---|---|---|---|---|
| Time Series and Forecasting Methods | 3 | 0 | 0 | 3 |
| Stochastic Systems: Reliability, Modelling and Simulation | 3 | 1 | 0 | 4 |
| Elective-III | 3 | 0 | 0 | 3 |
| Project II | 0 | 0 | 0 | 10 |
- Elective I
- Business Analytics
- Statistical and Numerical Computing
- Artificial Intelligence for Data Science
- Information Theory and MaxEnt Methods
- Blockchain
- Elective II
- Marketing Analytics
- Data Streams: Models and Algorithms
- Measure Theory and Probability
- Game Theory and Strategy
- Environmental, Climate and Geospatial Analytics
- Elective III
- Computational Finance
- Statistical NLP and LLM
- Sampling Survey Methodology
- Statistical Learning Theory
- Probabilistic Causal Models
Eligibility
It is expected that students intending to join the program are passionate about learning mathematical, statistical, computational sciences along with artificial intelligence and data science.
Educational Background
- Bachelor’s degree with Mathematics as one of the subjects (preferably)
- B.Sc. in Mathematics/Physics/Statistics/Computer Science
- B.Tech./B.E./BCA/MCA (any discipline)
- Minimum marks: 50%
Selection Criteria
- Merit-based (UG percentage)
- Interview / Counselling
