Massachusetts Institute of Technology

What you'll study

The programme combines core topics from microeconomics and econometrics with computer science foundations. Typical areas covered include algorithm design and analysis, machine learning, probabilistic modelling, optimisation, market and mechanism design, game theory, causal inference, and large-scale data systems.

Coursework mixes formal theory and applied projects. You can expect to study subjects such as advanced microeconomic theory, empirical methods for causal inference and programme evaluation, algorithmic game theory, statistical learning, distributed systems and databases, and computational complexity. Many students also take elective modules in areas like natural language processing, reinforcement learning, industrial organisation, behavioural economics, and cryptoeconomics.

Learning formats include lectures, problem sets, data-oriented labs and research seminars. A substantive component of the degree is independent research or a capstone project carried out under the supervision of faculty from the Department of Economics, the Computer Science and Artificial Intelligence Laboratory (CSAIL) or related centres. Projects frequently use real-world datasets and often involve collaboration with research groups, labs or industry partners at MIT.

Entry requirements

Applicants normally hold a strong undergraduate degree in computer science, economics, mathematics, statistics, engineering or a closely related quantitative field. Essential preparation includes calculus and linear algebra, probability and statistics, programming experience (Python, C++ or similar), and formal exposure to algorithms or theoretical computer science.

Admissions assess academic transcripts, letters of recommendation that speak to research or quantitative ability, a personal statement outlining research interests and goals, and relevant project or work experience. Applicants aiming to undertake research should demonstrate prior experience with empirical or theoretical work; evidence of programming and data-analysis projects is highly valued. International applicants must meet MIT's standard requirements for English proficiency where applicable.

Career prospects

Graduates enter a wide range of roles that sit at the intersection of economics and computing. Common destinations include data scientist or machine learning engineer roles in technology firms, quantitative researcher positions at finance and fintech companies, product and policy roles involving marketplace design, and economic modelling positions in consultancies and government agencies.

The programme also provides strong preparation for further academic research; graduates often progress to doctoral programmes in economics, computer science or interdisciplinary fields such as computational social science. Additionally, the MIT entrepreneurial ecosystem supports alumni who choose to found or join startups focused on ad-tech, algorithmic trading, platform markets or data-driven public-policy tools.

Why study at Massachusetts Institute of Technology

MIT offers exceptional depth in both computer science and economics, with close collaboration between departments and research centres such as CSAIL, the Department of Economics, the Laboratory for Information and Decision Systems and interdisciplinary initiatives in data science and social science. Students benefit from access to faculty who are leaders in algorithmic theory, machine learning, econometrics and market design.

The institute’s strong ties to industry, an active seminar and workshop culture, and an extensive research infrastructure provide ample opportunity for applied projects and internships. MIT’s entrepreneurial support—through accelerators, industry partnerships and alumni networks—also makes it an attractive place to turn research into products or policy impact.