Marymount University

What you'll study

This programme emphasises numerical analysis, algorithm development and mathematical modelling alongside applied topics that connect mathematics to computation. Typical study areas include numerical linear algebra, numerical solutions of ordinary and partial differential equations, optimisation and control, scientific computing, high‑performance computing, stochastic modelling and introductory machine learning methods.

Students follow a taught curriculum with a choice of electives and a substantial culminating experience. You will normally complete coursework in core computational topics and choose electives that may cover advanced topics such as spectral methods, computational fluid dynamics, inverse problems, data assimilation and computational statistics. The programme culminates in either a capstone project or a research thesis that applies computational techniques to an applied problem in science, engineering or finance.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related discipline. Typical preparation includes undergraduate courses in calculus, linear algebra, differential equations and introductory programming. Departments typically look for evidence of quantitative ability in transcripts and supporting documents.

• Academic transcripts from all post‑secondary institutions attended.
• A personal statement explaining your interest in computational mathematics and relevant experience.
• One or more letters of recommendation from academic or professional referees familiar with your quantitative work.
• Applicants whose undergraduate preparation lacks particular prerequisites may be offered conditional admission and advised to complete specified bridging courses before fully progressing in the programme.

Career prospects

Graduates are prepared for technically demanding roles that require both mathematical rigour and computational skill. Common career paths include data scientist, quantitative analyst, computational scientist, simulation and modelling engineer, software developer for scientific applications, and roles in operations research or optimisation. The degree also provides solid preparation for further graduate study such as a PhD in applied mathematics, computational science or related fields.

Because Marymount is in the Washington, D.C. region, students often pursue internships and employment with federal agencies, defence contractors, research laboratories, financial services firms and technology companies in the metro area, applying numerical and computational methods to practical problems.

Why study at Marymount University

Marymount offers small class sizes and close faculty mentoring, which can be especially valuable for project‑oriented work in computational mathematics. The university’s location near the nation’s capital provides access to internships, collaborative research and employment opportunities with government laboratories, agencies and local tech and consulting firms.

The programme emphasises hands‑on computing and applied problem solving; students gain experience with scientific programming, numerical libraries and high‑performance computing environments alongside theoretical foundations. Supportive faculty advising helps students tailor the degree toward industry applications, interdisciplinary projects, or preparation for doctoral study.