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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
Interested students must meet the following minimum eligibility criteria for B.Tech Computer Science Economics: Minimum 50% Marks in Class X and XII. Along with 50 % in PCM (Physics/Chemistry and Mathematics) in Class XII
Selection Criteria
Selection for admission to the B.Tech. Computer Science Economics program at UPES School of Computer Science relies on the performance of individual candidates in UPESEAT / JEE Mains / Board Merit / SAT/ CUET-UG
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Further Information
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Frequently Asked Questions
While traditional Computer Science Engineering focuses mainly on software development and computing, Computer Science and Economics combines programming with economic theory, data analytics, and financial systems. This interdisciplinary approach prepares students to solve technology-driven business and economic challenges across industries like FinTech, consulting, and digital finance.
The B.Tech Computer Science and Economics syllabus includes programming, data structures, algorithms, artificial intelligence, machine learning, databases, statistics, calculus, microeconomics, macroeconomics, econometrics, game theory, and financial economics. Students also work on projects and industry-oriented electives to gain practical experience.
Yes. Computer Science and Economics is designed to balance both disciplines. You'll learn programming, software development, AI, and data science alongside economics, finance, statistics, and quantitative analysis. This combination helps students understand how technology can solve real-world economic and business problems.
Graduates can explore diverse Computer Science and Economics jobs including promising opportunities in FinTech, such as Data Scientist, Software Developer, Business Analyst, Quantitative Analyst, Financial Data Analyst, AI Engineer, Product Analyst, FinTech Developer, Risk Analyst, and Economic Research Associate. The interdisciplinary curriculum opens opportunities across technology, finance, FinTech, consulting, and analytics sectors.
Absolutely. Computer Science and Economics equips students with programming, AI, data analytics, financial modelling, and economic analysis skills that are highly valued in FinTech and quantitative finance. Graduates can build intelligent financial applications, develop trading models, and analyze large financial datasets using technology.
Yes. The program provides a strong foundation in programming, mathematics, statistics, machine learning, and economics, making graduates well-suited for Data Scientist and Quantitative Analyst roles. These are among the fastest-growing Computer Science and Economics jobs across FinTech, finance, banking, technology, and consulting industries.
Graduates in Computer Science and Economics can build careers across FinTech, banking, investment firms, consulting, e-commerce, technology, and insurance. Popular Computer Science and Economics jobs include Data Economist, Risk Analyst, FinTech Analyst, Market Design Specialist, Algorithmic Trading Analyst, and Policy AI Specialist. Top recruiters include McKinsey, RBI, Google, Paytm, Razorpay, and JPMorgan.
Students develop programming, software engineering, data analysis, machine learning, economic modelling, financial analytics, problem-solving, quantitative reasoning, and decision-making skills. The Computer Science and Economics curriculum also strengthens analytical thinking, helping graduates bridge technology with business and economic strategy.
Yes. Mathematics forms the foundation of the B.Tech Computer Science and Economics syllabus. Students use calculus, linear algebra, probability, and statistics in programming, machine learning, data science, econometrics, and financial modelling. A strong mathematical background helps in solving complex computational and economic problems.
After completing Computer Science and Economics, graduates can pursue M.Tech, MS, MBA, MCA, or master's programs in Data Science, Artificial Intelligence, Economics, Financial Engineering, Quantitative Finance, or Business Analytics. Higher studies can enhance specialization and improve Computer Science and Economics salary prospects across global industries.
