M.Sc. Mathematics and Data Science
Programme Overview
The M.Sc. Mathematics and Data Science programme at MIT-WPU is a multidisciplinary degree programme that combines elements of pure and applied mathematics, statistics, and computer science. The programme provides students with a strong foundation in mathematical concepts and techniques, as well as the skills and knowledge needed to design and develop software applications.
The programme also prepares students for research roles in academia and industry, as well as for careers in fundamental research. The curriculum involves topics such as mathematical modeling, data analysis, and programming, as well as hands-on experience working on research projects or internships.
Duration & Fees
Duration
2 Years
Applications Open for 2026
Fee Per Year
₹ 1,10,000
Scholarship
| Scholarship for AY 2026-27 | Graduation Aggregate Score | XII Aggregate Score |
|---|---|---|
|
Dr. Vishwanath Karad Scholarship |
85 & Above |
85 & Above |
|
MIT-WPU Scholarship I |
80 & Above |
80 & Above |
|
MIT-WPU Scholarship II |
75 & Above |
75 & Above |
Note: Scholarships will be awarded based on the Graduation and Class 12/HSC score.
Terms & Conditions Apply:
- All Scholarships are awarded on a First Come First Serve basis. All Scholarships are awarded as fee adjustments.
- To continue the scholarship for the entire duration of the programme, a minimum level of the academic score has to be maintained at an 8 CGPA across all semesters, attendance is to be maintained at a minimum of 80 percent and there should be no disciplinary action against the student.
For more detailed information visit our website: https://mitwpu.edu.in/scholarships
Eligibility
Minimum 50% aggregate marks in 3/4-year graduation degree in B. Sc. /B.Sc.(Hons) in Mathematics/ Statistics/ Data Science/ Machine Learning or equivalent degree or B.E./B.Tech./B.Sc. Agri/ B.Sc. Computer Science. Candidates with degrees other than B.Sc. Mathematics must have studied Mathematics as a regular subject in their undergraduate programme from a UGC-approved institution/university or its equivalent (minimum 45% for candidates belonging to the Reserved Category from Maharashtra State).
Selection Process
Admission will be based on the Personal Interaction (PI) score, conducted by the University according to the prescribed schedule
Programme Highlights
- Highly qualified faculty, with a strong track record of research and publication
- Workshops for hands-on experience in software such as R, SAS, Python, HADOOP, SQL, SPSS
- Guest lectures, seminars, and workshops by eminent corporate leaders
- MOOCs, skill enhancement and interdisciplinary tracks for holistic development
- Dedicated Centre for Industry-Academia Partnerships to support students for internships and job placements.
- MIT-WPU Technology Business Incubator (TBI) to support early-stage entrepreneurs, and students through funding, mentoring, and network connection
- Rural, National and International immersion programmes
- Six-Month Intensive Training for NET/SET/GATE Exam Success
Programme Structure
| Sr. No | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Linear Algebra | PF | 2 |
|
2 |
Real Analysis | PF | 4 |
|
3 |
Probability Theory | PM | 4 |
|
4 |
Python | PM | 1 |
|
5 |
Research Methodology | PM | 4 |
|
6 |
Program Elective-01 | PE | 4 |
|
7 |
Data Science Lab – 01 | PM | 2 |
|
8 |
Scientific Studies of Mind, Matter, Spirit and Consciousness | UC | 2 |
|
9 |
Yoga | UC | 1 |
|
Total Credits |
24 | ||
| Sr. No | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Differential Equations | PF | 3 |
|
2 |
Measure Theory | PM | 4 |
|
3 |
Introduction to Data Science | PM | 3 |
|
4 |
Statistical Inference | PM | 2 |
|
5 |
Data Based Management Systems (DBMS) | PM | 1 |
|
6 |
Program Elective-02 | PE | 4 |
|
7 |
Data Science Lab – 02 | - | 4 |
|
8 |
Peace Building: Global Initiatives | UC | 2 |
|
Total Credits |
23 | ||
| Sr. No | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Advanced Calculus | PM | 3 |
|
2 |
Functional Analysis | PM | 4 |
|
3 |
Data Mining | PM | 3 |
|
4 |
Machine Learning | PM | 3 |
|
5 |
Program Elective-03 | PE | 4 |
|
6 |
Machine Learning Lab | PM | 2 |
|
7 |
Mini Project | PR | 2 |
|
Total Credits |
21 | ||
| Sr. No | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Financial Mathematics | PM | 4 |
|
2 |
Program Elective-04 | PE | 4 |
|
3 |
Project / Industrial Internship | PR | 12 |
|
Total Credits |
20 | ||
| Semester | Name of the Course | Type |
|---|---|---|
|
1 |
Group Theory | Program Elective - 01 |
|
2 |
Computational Statistics | Program Elective - 01 |
|
3 |
Numerical Methods | Program Elective - 01 |
|
4 |
Ring Theory & Field Theory | Program Elective - 02 |
|
5 |
Stochastics Processes | Program Elective - 02 |
|
6 |
Data Visualization & Explanatory Analysis | Program Elective - 02 |
|
7 |
Operations Research | Program Elective - 03 |
|
8 |
Multivariate Analysis | Program Elective - 03 |
|
9 |
Soft Computing | Program Elective - 03 |
|
10 |
Actuarial Mathematics | Program Elective - 04 |
|
11 |
Time Series | Program Elective - 04 |
|
12 |
Artificial Intelligence | Program Elective - 04 |
Career Prospects
Biostatistician
Content Analyst
Data Analyst
Data Engineer
Data Governance Analyst
Data Visualization Engineer
Data Scientist
Business Analysts
Financial Analyst
Decision Scientist
Business Intelligence Analyst
Marketing Analytics
Manager Product Analyst
Quantitative Analysts
Risk Analyst
Statistical Analyst
Statistician
Scientists at Government Organisations (like DRDO, ARDE, IITM & ISRO etc.)
Research Fellow at IITs, IISERs, IIMs etc
Assistant Professors, Lecturers, and Teachers
Bank Probationary Officers
Programme Outcomes
- Build a strong foundation in advanced mathematical concepts, applied mathematics, statistics, and computer science, enabling graduates to tackle complex analytical problems.
- Acquire practical skills in data analysis, mathematical modeling, and programming, preparing students to design and develop software applications and solutions for real-world challenges.
- Develop the ability to apply mathematical and statistical techniques in data-driven decision-making across diverse sectors such as technology, finance, healthcare, and research.
- Gain hands-on experience through research projects, internships, and industry collaborations, enhancing readiness for both academic research and professional roles.
- Cultivate critical thinking, logical reasoning, and problem-solving abilities essential for careers as data scientists, analysts, researchers, and educators.
- Demonstrate effective communication and teamwork skills for interdisciplinary collaboration in both academic and industry settings.
- Prepare for lifelong learning and adaptability to emerging trends in mathematics, data science, artificial intelligence, and machine learning.
FAQs
Students are trained in languages and tools commonly used in data science, such as Python, R, MATLAB, SQL, and relevant data visualisation and machine learning libraries.
Yes, the curriculum encourages interdisciplinary projects and electives, allowing students to apply mathematical and data science techniques in fields like finance, healthcare, and engineering.
Yes, students work on real-world datasets during the programme, projects, and internships, gaining hands-on experience in data cleaning, analysis, and interpretation.
MIT-WPU has a dedicated placement cell that offers training, resume-building workshops, mock interviews, and connects students with top recruiters in analytics, IT, finance, and research.
admissions@mitwpu.edu.in
