Massachusetts Institute of Technology

What you'll study

The curriculum combines core mathematical foundations with applied and computational topics. Early-year subjects typically include multivariable calculus, linear algebra, differential equations and introductory probability and statistics. Mid- and upper-level work moves into real and complex analysis, numerical analysis, partial differential equations, dynamical systems and optimisation, alongside courses in mathematical modelling and scientific computing.

Students are able to complement their mathematics studies with electives from computer science, physics, engineering, economics and statistics to build domain-specific modelling skills. Practical experience is emphasised through programming projects, computational laboratories and research opportunities (for example through undergraduate research programmes), enabling hands-on work with data, simulations and algorithmic implementation.

• Core topics: calculus, linear algebra, real analysis, complex analysis
• Applied topics: numerical analysis, PDEs, dynamical systems, optimisation, stochastic processes
• Computational skills: scientific computing, numerical linear algebra, algorithmic methods and high-level programming for computation
• Interdisciplinary electives: machine learning, mathematical finance, computational physics, control theory, statistics
• Capstone and research: project courses, independent studies and supervised undergraduate research opportunities

Entry requirements

Applicants are expected to have a very strong foundation in secondary-school mathematics. Typical preparation includes advanced coursework in calculus and, where available, further topics such as multivariable calculus, linear algebra and probability. Courses or experience in physics and computing are advantageous.

Admission is competitive and evaluated holistically: academic achievement, quality of mathematical work, problem-solving ability, teacher or mentor recommendations and evidence of motivated engagement with mathematical ideas all matter. Successful candidates typically demonstrate strong analytical skills, sustained interest in mathematics and the ability to handle rigorous, proof-based material.

Career prospects

Graduates with an applied mathematics background move into a wide range of careers that rely on quantitative problem-solving. Common paths include data science and analytics, software engineering, quantitative finance and risk, actuarial roles, technical consulting, modelling and simulation in engineering or physical sciences, and roles in government or research laboratories.

Many graduates continue to further study in mathematics, statistics, computer science, engineering or other specialised graduate programmes, while others enter industry straightaway, where their combination of theory, computation and modelling skills is highly valued.

Why study at Massachusetts Institute of Technology

Studying applied mathematics at MIT provides access to a rigorous departmental curriculum taught by internationally recognised faculty and researchers. The Institute’s strength in computing, engineering and the physical sciences makes it straightforward to pursue interdisciplinary coursework and collaborative projects.

Undergraduates benefit from extensive research and experiential-learning opportunities, strong ties with industry and technology sectors in the Boston/Cambridge area, and a culture that encourages hands-on problem solving. The combination of theoretical depth and practical computation prepares graduates for demanding technical roles and for advanced study.