B.Tech in Computation & Mathematics
A 4-year undergraduate programme integrating the precision of mathematics with the power of computation to prepare students for scientific & analytical careers.
B.Tech in computation & mathematics overview
We develop problem-solvers who unite theory and computation to drive real-world innovation, grounded in mathematics, algorithms, and scientific computing for impact across AI, data science, optimisation, and quantitative research. Students experience:
Course grounded firmly
in advanced mathematical disciplines such as linear algebra, complex analysis, enabling conceptual mastery beyond routine engineering maths
Experiential computational
and programming training through data structures, graph algorithms, cryptography and computational biology, all taught alongside mathematical models
Interdisciplinary specialisation
including advanced electives in quantum computing, computational biology, and cryptography enable exploration of frontier scientific domains
Research and innovation
culture through mathematical modelling projects, research internships, and capstone collaborations, to develop analytical creativity and industry-ready expertise
Programme details
Academic structure
Our academic structure is designed to establish robust foundations, followed by increasing specialization in later years.
PIE CHART: CREDIT DISTRIBUTION FOR COMPUTATION & MATHEMATICS
- Duration: 4 years / 8 semesters
- Total credits & degree requirement: The programme requires not less than 165 credits to be awarded a B.Tech degree.
- Focus areas:
- Data science/Data analytics
- Financial mathematics
- Theoretical computer science/Mathematical sciences
- Cryptography
- Quantum computing
| Course | L-T-P | Credits |
|---|---|---|
| Calculus and ODE | 4-1-0 | 5 |
| Discrete Mathematics | 2-0-0 | 2 |
| Introduction to Electrical & Electronics | 2-1-2 | 4 |
| Introduction to Computing | 3-0-2 | 4 |
| Earth & Environmental Sciences | 2-0-0 | 2 |
| Media Project | 0-0-3 | 1.5 |
| English | 3-0-0 | 3 |
| Introduction to Entrepreneurship | 0-0-2 | 1 |
| French I | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Complex Analysis (Fractal) | 3-1-0 | 2 |
| Linear Algebra | 3-1-0 | 4 |
| Classical & Quantum Mechanics | 2-1-2 | 4 |
| Data Structures | 3-0-2 | 4 |
| Optimization Techniques | 4-1-0 | 5 |
| Entrepreneurship Practice (Fractal) | 0-0-2 | 1 |
| Professional Ethics (Fractal) | 0-1-0 | 1 |
| French II | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Probability & Statistics | 3-1-0 | 4 |
| Signals and Systems | 3-0-2 | 4 |
| Real Analysis | 3-1-0 | 4 |
| Design and Analysis of Algorithms | 3-0-2 | 4 |
| Algebra | 3-1-0 | 4 |
| Lean Start Up (Fractal) | 0-0-3 | 1 |
| Principles of Economics | 1-1-0 | 1.5 |
| French III | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Numerical Methods | 3-0-2 | 4 |
| Number Theory & Cryptography | 3-1-0 | 4 |
| Statistical Methods | 2-0-2 | 3 |
| Theory of Computation | 3-0-0 | 3 |
| Mathematical Foundations for Machine Learning | 3-0-2 | 4 |
| Stochastic Processes | 3-0-0 | 3 |
| Design Thinking | 1-0-2 | 2 |
| Financial Accounting (Fractal) | 1-1-0 | 1.5 |
| French IV | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| PDE: Theory and Computation | 3-1-0 | 4 |
| Operating Systems | 3-0-2 | 4 |
| Database Management System | 3-0-2 | 4 |
| Object Oriented Programming | 2-0-2 | 3 |
| Financial Mathematics | 3-0-2 | 4 |
| Open Elective I | 3-0-0 | 3 |
| Liberal Arts Elective I | 2-0-0 | 2 |
| French V (Optional) | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Quantum Computing | 3-0-0 | 3 |
| Computer Networks | 3-0-2 | 4 |
| Open Elective II | 3-0-0 | 3 |
| Open Elective III | 3-0-0 | 3 |
| Open Elective IV | 3-0-0 | 3 |
| Liberal Arts Elective II | 2-0-0 | 2 |
| Introduction to Professional Development | 2-0-0 | 2 |
| French VI (Optional) | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Open Elective V | 3-0-0 | 3 |
| Open Elective VI | 3-0-0 | 3 |
| Open Elective VII | 3-0-0 | 3 |
| Open Elective VIII | 3-0-0 | 3 |
| Liberal Arts Elective III | 2-0-0 | 2 |
| French VII (Optional) | 0-2-0 | 0.5 |
| Course | L-T-P | Credits |
|---|---|---|
| Project | 0-9-12 | 15 |
| French VIII (Optional) | 0-2-0 | 0.5 |
FAQs
The programme is designed to build dual expertise, nurturing students who can think mathematically and code computationally. This rare combination enables them to excel in data science, AI, quantitative finance, research, and advanced analytics roles across industries.
This programme is ideal for curious, analytical thinkers who enjoy logic, problem-solving, and exploring how mathematical theory powers modern technology. It develops both conceptual understanding and practical application, preparing students for innovation-led careers.
While traditional computer science focuses primarily on coding, computation & mathematics deepens students’ understanding of the mathematical principles driving algorithms and models. This gives them a strong conceptual edge in high-end computational research and emerging tech domains.
Students engage in research internships, mathematical modelling workshops, and interdisciplinary capstone projects in collaboration with Mahindra University’s research centres and industry partners, offering real-world exposure from early on.
Graduates are well-prepared for careers in data science, operations research, financial analytics, cybersecurity, AI research, and academia. Many also pursue advanced studies at leading international universities.
