University of Minnesota

What you'll study

The programme blends core mathematics courses with computational and applied modules to develop both theoretical understanding and numerical proficiency. Early years concentrate on calculus, multivariable calculus, linear algebra and differential equations, alongside introductory programming. Intermediate and advanced study typically includes real analysis, abstract algebra, probability and statistics, and numerical analysis.

Computational-specific topics commonly studied as part of the track or elective choices include numerical linear algebra, scientific computing, numerical methods for partial differential equations, optimisation algorithms, algorithm analysis, and high-performance computing techniques. Coursework emphasises the use of programming languages and tools (for example Python, MATLAB, or C/C++), computational experiments, and reproducible workflows. Many students take complementary modules in statistics, computer science, physics or engineering to broaden applied skills.

The degree often culminates in a capstone experience or undergraduate research project where students apply mathematical modelling and computational techniques to a real problem, working closely with faculty or industry partners.

Entry requirements

Applicants are expected to have a strong background in high-school mathematics, including algebra, geometry and pre-calculus; successful applicants typically present experience with calculus. Admissions consider overall academic record, recommendations and evidence of quantitative ability. Familiarity with high-school or introductory university-level programming is advantageous but not always required.

Domestic applicants must meet the University of Minnesota's general undergraduate admission standards; international applicants must meet academic equivalency requirements and provide proof of English language proficiency where required. Transfer applicants are evaluated on college coursework in mathematics and related subjects; completion of calculus and introductory linear algebra can strengthen an application.

Career prospects

Graduates with a computational mathematics specialism are well placed for careers that require quantitative modelling and software-based problem solving. Typical roles include data scientist or analyst, quantitative analyst (finance), software engineer, scientific or computational researcher, machine learning engineer, and roles in simulation and modelling for engineering or physical sciences.

Many graduates also pursue further study — master's or doctoral programmes in mathematics, applied mathematics, statistics, computer science, engineering or computational science. The blend of theory and computational practice is valued across industry sectors such as technology, finance, energy, defence, pharmaceuticals and national laboratories.

Why study at University of Minnesota

The University of Minnesota offers a large and active mathematics department with faculty working across pure and applied fields, enabling students to find supervisors and mentors for a wide range of computational topics. The campus provides access to substantial computing infrastructure and interdisciplinary research centres, facilitating projects that combine mathematics with computer science, engineering and physical sciences.

Undergraduate students benefit from opportunities for research with faculty, internship links in the Twin Cities’ diverse economy, and a broad selection of complementary courses across departments. Support services for undergraduates, student mathematical societies and seminars create a collaborative environment for developing both theoretical depth and practical programming expertise.