Massachusetts Institute of Technology

What you'll study

This programme blends the mathematical depth of an undergraduate mathematics curriculum with core and advanced computer science topics. You will take a sequence of core mathematics subjects—typically including multivariable calculus, linear algebra, real analysis, probability and statistics, and abstract algebra—alongside computing fundamentals such as programming, data structures, discrete mathematics, algorithms and automata theory.

Beyond the core, students choose from a wide array of electives in areas such as numerical analysis, optimization, combinatorics, graph theory, topology, logic and set theory on the mathematics side; and in computer science: algorithms, complexity theory, machine learning, artificial intelligence, computer systems, databases, cryptography and programming language design. Applied options and interdisciplinary subjects (for example computational biology, mathematical finance or scientific computing) are also available.

The curriculum typically emphasises rigorous proof-based training as well as practical implementation and experimental work. Many students participate in supervised undergraduate research (UROP) or technical projects, and may complete an optional advanced thesis or capstone project that integrates mathematics and computing.

Entry requirements

Successful applicants demonstrate strong preparation in mathematics—usually through several years of secondary-level work in calculus and algebra—and evidence of programming experience or computer science coursework. Admissions at MIT are highly selective and look for excellence across academic records, teacher and counsellor recommendations, and personal qualities such as intellectual curiosity and initiative.

Typical preparation includes:

• High achievement in advanced mathematics courses (calculus, linear algebra where available) and strong problem-solving skills.
• Experience with programming and computational thinking (projects, coursework or self-directed study in languages such as Python, Java, C/C++ or similar).
• Evidence of engagement beyond the classroom—math competitions, programming contests, research projects, or independent technical work are beneficial.

Career prospects

Graduates with a combined mathematics and computer science degree are in demand across technology, finance, research and industry. Common career paths include software engineering, data science and machine learning engineering, quantitative analysis in finance, cryptography and security, algorithm and systems research, and roles in scientific computing.

Many alumni pursue graduate study in computer science, applied mathematics, statistics or related fields; others join startups, large technology firms, consultancies, research labs or government agencies. The balance of theoretical skill and practical coding ability also prepares graduates for roles that require modelling, optimisation and rigorous analysis of complex systems.

Why study at Massachusetts Institute of Technology

The Institute offers an unusually rich environment for a mathematics-and-computer-science education: close proximity between the Department of Mathematics and the Computer Science and Artificial Intelligence Laboratory (CSAIL) and EECS allows students to combine leading-edge theoretical instruction with access to experimental research and interdisciplinary labs.

Opportunities such as the Undergraduate Research Opportunities Program (UROP), cross-department collaboration, mentoring by internationally recognised faculty, and a strong entrepreneurial ecosystem provide practical experiences that complement coursework. The curriculum’s emphasis on both proof-oriented mathematics and hands-on software development equips students to tackle foundational research questions or to apply quantitative methods in industry.

Finally, the institution’s culture of collaboration and problem solving—together with extensive career support and alumni networks—helps students translate technical preparation into impactful careers or follow-on graduate study.