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B.Tech. Computer Science & Economics
Program details
The B.Tech. in Computer Science & Economics (CS Econ) is a four-year, ~160-credit program that brings together core Computer Science and core Economics to help students model real-world markets using computation. Built around computational finance, game theory and machine learning, the curriculum balances foundational coursework with electives, capstone projects and internships, delivered through project-based, interdisciplinary, data-powered learning.
A key differentiator is its emphasis on mechanism design (a specialised area of game theory), where students learn to build “truthful” auctions and efficient matching systems—skills relevant to pricing and allocation problems such as surge-pricing models or ad-bidding engines. The program also develops “quantamental” capability—combining data, algorithms and rigorous economic theory—so students can apply models to real datasets across sectors such as energy, logistics and infrastructure.
As AI systems increasingly participate in buying, selling and optimisation, the program includes a unique focus on AI alignment and incentive engineering through computational incentives—training students to reduce the risk of models “gaming the system”. It is designed for the “techno-social thinker”: someone who enjoys maths and coding, and is equally curious about how markets move, how platforms grow, and how incentives shape outcomes.
Program Highlights
- Integrated tech + economics (algorithms, data science and economic theory in one degree)
- Strong quantitative rigour through mathematics, econometrics and modelling
- Data-driven decision-making with analytics, ML and forecasting built in
- FinTech lab exposure (trading platforms, blockchain applications, financial APIs)
- Industry-driven electives such as Behavioural Economics with AI and Platform Design
- Startup support via incubation and mentorship for FinTech/SaaS ideas
Industry Trends & Career Opportunities
As markets, platforms and policy become increasingly data-led, demand is rising for professionals who can build systems and also explain incentives, pricing, risk and behaviour. The program aligns with shifts such as computational economics and market design in digital marketplaces, ML-powered econometrics for forecasting/optimisation, and modelling for risk analytics, consumer prediction, pricing and ESG decision support.
The salary ranges between ₹12-35 LPA and vary by location, skills and internships. Indicative roles and employers include:
- Data Economist / Risk Analyst — employers: McKinsey, RBI, Google (Economics/Ads)
- FinTech Analyst / Market Design / Algorithmic Trading — employers: Paytm, Razorpay, JPMorgan
- Policy AI Specialist / Policy Analyst — employers: NITI Aayog, Deloitte, Meta
- Quant Analyst / Quant Developer — employers: Goldman Sachs, quant firms
- Behavioural Economist (Tech) / Product Analyst & Strategy — employers: Flipkart, Uber (Economics/Marketplace)
Placements
Placement support typically includes a dedicated career cell for CS and Economics profiles, internships with banks/policy institutions/analytics firms, and hiring that values demonstrable projects (capstones, modelling work, experimentation and product analytics). The program is positioned for recruiters as a blend of algorithmic capability + economic reasoning + market/product clarity.
Expected recruiter clusters :
- Tech & platforms: Google, Amazon, Meta
- Consulting & finance: McKinsey & Co, BCG, Goldman Sachs, JP Morgan, HSBC
- FinTech: Zerodha, Razorpay, Pine Labs, PayPal
- Policy research & think tanks (plus public-sector analytics roles)
Fee Structure
Click here for detailed Fee Structure.
Curriculum
Engineering & Economic Foundations
The first year focuses on the "Common Engineering Core" required by AICTE, with early introduction to Economic Principles.
Semester 1
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Engineering Mathematics - I (Linear Algebra & Calculus) | 3 | 1 | 0 | 4 |
| Engineering Physics (with Lab) | 3 | 0 | 2 | 4 |
| Programming for Problem Solving (C/Python) | 3 | 0 | 4 | 5 |
| Principles of Microeconomics | 3 | 0 | 0 | 3 |
| Professional Communication Skills | 2 | 0 | 2 | 3 |
| TOTAL | 19 |
Semester 2
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Engineering Mathematics - II (ODE & Multivariable Calculus) | 3 | 1 | 0 | 4 |
| Engineering Chemistry (with Lab) | 3 | 0 | 2 | 4 |
| Data Structures & Algorithms (with Lab) | 3 | 0 | 4 | 5 |
| Principles of Macroeconomics | 3 | 0 | 0 | 3 |
| Workshop & Manufacturing Practices | 1 | 0 | 4 | 3 |
| TOTAL | 19 |
Year 2: Core Interdisciplinary Integration
The transition from general engineering to specialized computational economics begins here.
Semester 3
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Discrete Mathematical Structures | 3 | 1 | 0 | 4 |
| Object-Oriented Programming (Java/C++) | 3 | 0 | 2 | 4 |
| Intermediate Microeconomics (Mathematical) | 3 | 1 | 0 | 4 |
| Econometrics - I (Statistical Inference) | 3 | 0 | 2 | 4 |
| Digital Logic & Computer Organization | 3 | 0 | 0 | 3 |
| TOTAL | 19 |
Semester 4
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Design and Analysis of Algorithms | 3 | 0 | 2 | 4 |
| Operating Systems (with Lab) | 3 | 0 | 2 | 4 |
| Econometrics - II (Time Series & Forecasting) | 3 | 0 | 2 | 4 |
| Intermediate Macroeconomics | 3 | 1 | 0 | 4 |
| Database Management Systems | 3 | 0 | 2 | 4 |
| TOTAL | 20 |
Year 3: Specialized Computational Economics
Focus shifts to "Mechanism Design" and "FinTech," where code meets market theory.
Semester 5
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Game Theory & Strategic Behavior | 3 | 1 | 0 | 4 |
| Artificial Intelligence & Machine Learning | 3 | 0 | 2 | 4 |
| Computational Finance & Algorithmic Trading | 2 | 0 | 4 | 4 |
| Computer Networks | 3 | 0 | 0 | 3 |
| Professional Elective - I (See Baskets) | 3 | 0 | 0 | 3 |
| Mini Project (Market Simulation) | 0 | 0 | 4 | 2 |
| TOTAL | 20 |
Semester 6
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Mechanism Design & Auction Theory | 3 | 1 | 0 | 4 |
| Econometrics with Machine Learning | 3 | 0 | 2 | 4 |
| Professional Elective - II | 3 | 0 | 0 | 3 |
| Open Elective - I (Management/Law) | 3 | 0 | 0 | 3 |
| Environmental Sciences (Mandatory Audit) | 2 | 0 | 0 | NC |
| Summer Internship (Industry/Research) | – | – | – | 3 |
| TOTAL | 17 |
Year 4: Synthesis & Industry Readiness
Final year is dedicated to high-level electives and a significant Capstone project.
Semester 7
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Behavioral Economics & Digital Platforms | 3 | 0 | 0 | 3 |
| Professional Elective - III | 3 | 0 | 0 | 3 |
| Professional Elective - IV | 3 | 0 | 0 | 3 |
| Open Elective - II | 3 | 0 | 0 | 3 |
| Capstone Project Phase - I | 0 | 0 | 12 | 6 |
| TOTAL | 18 |
Semester 8
| Course | L | T | P | Credit |
|---|---|---|---|---|
| Full Semester Industry Internship / Major Project | 0 | 0 | 24 | 12 |
| TOTAL | 12 |
Eligibility
The minimum eligibility criteria for B.Tech. Computer Science & Economics (CS Econ) to be fulfilled by interested students is as follows: Minimum 60–70% aggregate marks in Class XII with Physics, Chemistry and Mathematics (PCM), with strong performance in Mathematics due to the program’s quantitative economics component.
Selection Criteria
Selection is based on performance in JEE Main/CUET (UG)/UPESEAT, followed by a Logical & Quantitative Aptitude Test and a Personal Interview.
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