University of North Dakota

What you'll study

This degree combines core mathematical theory with applied and computational subjects. You will study core modules such as calculus, linear algebra, differential equations, real analysis and probability. Computational and applied topics typically include numerical analysis, scientific computing, mathematical modelling, algorithms and data structures, and computational linear algebra. Programming and software are integral: expect sustained exposure to languages and tools used in computational work (for example Python, MATLAB, C/C++ and use of numerical libraries), plus instruction in version control and reproducible computing.

The course of study usually progresses from foundational mathematics in the early years to more specialised and project-based work later on. Typical fourth-year components include a capstone project or undergraduate research in computational mathematics, electives in areas such as partial differential equations, optimisation, high-performance computing and statistical learning, and opportunities for interdisciplinary study with physics, engineering, computer science or finance.

Entry requirements

Applicants are expected to have a strong background in high school mathematics; completion of pre-calculus and a year of calculus is commonly required or strongly recommended. Typical admissible students will show solid preparation in algebra, geometry and trigonometry and will benefit from coursework in AP Calculus or A-level Mathematics where applicable. Successful applicants to the programme usually present a competitive high-school record and standardised test scores where required by the university.

For transfer students or those with prior college study, a sequence of college calculus and an introductory linear algebra or discrete mathematics course will normally be expected. The department may also consider placement exams or departmental advising to guide appropriate starting courses. International applicants should meet the university's general admissions and English language proficiency requirements.

Career prospects

Graduates with a computational mathematics degree enter a wide range of careers where quantitative and programming skills are prized. Common pathways include data scientist or analyst roles, software and algorithm development, quantitative analyst positions in finance, actuarial work, and roles in modelling and simulation for engineering or physical sciences.

Other destinations include graduate study in applied mathematics, computer science, statistics or engineering, work in government laboratories and research institutes, and employment in industries such as energy, aerospace, healthcare analytics and technology firms. The degree also prepares students for roles requiring strong problem-solving and technical communication skills.

Why study at University of North Dakota

The University of North Dakota offers a mathematics programme with personalised attention from faculty who are active in both theoretical and applied research. Students benefit from accessible faculty mentorship, opportunities for undergraduate research, and project-based learning that integrates computing and modelling.

UND's location supports interdisciplinary collaboration with departments such as computer science, engineering and physics, and students can take advantage of computing resources and laboratory facilities. The department also supports internships and practicum experiences with regional industry and national research partners, helping students to translate classroom skills into employable experience.

Overall, the programme is designed to provide a balance of rigorous mathematical training and practical computational experience, preparing graduates for both the workforce and advanced study.