University of Missouri-Kansas City

What you'll study

The Computational Mathematics track emphasises numerical analysis, algorithm development and the use of high‑performance computing to solve applied problems. Students typically take a mix of core and elective modules that cover both theory and implementation. Course topics commonly include:

• Numerical Analysis and Approximation: error analysis, interpolation, numerical integration and differentiation.
• Numerical Linear Algebra: direct and iterative solvers, eigenvalue problems and preconditioning techniques.
• Scientific Computing: algorithm design, implementation in languages such as Python, MATLAB or C, and software engineering practices for numerical code.
• Computational Methods for PDEs: finite difference, finite element and spectral methods for time‑dependent and steady problems.
• Optimization and Inverse Problems: continuous and discrete optimisation, constrained optimisation and parameter estimation.
• Probability, Statistics and Stochastic Modelling: probabilistic modelling, Monte Carlo methods and uncertainty quantification.
• Advanced Analysis: real analysis or functional analysis topics underpinning numerical methods and convergence theory.

Program structure typically offers both thesis and non‑thesis (coursework) options. The thesis route includes independent research under a faculty supervisor and results in a written thesis, while the non‑thesis option emphasises additional coursework or a practicum/project component. Students have opportunities to use campus computing resources and to take interdisciplinary electives in engineering, computer science or statistics.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics or a closely related field (applied mathematics, engineering, physics, computer science). Successful applicants normally have strong preparation in calculus, linear algebra, differential equations and introductory real analysis. Typical application materials include:

• Official academic transcripts demonstrating relevant undergraduate coursework.
• A statement of purpose describing academic background, research or professional interests, and reasons for pursuing the programme.
• Letters of recommendation from academic or professional referees who can speak to quantitative ability and potential for graduate‑level work.
• A current CV or résumé outlining relevant experience, research, or programming skills.
• Proof of English language proficiency (such as TOEFL or IELTS) for international applicants who are not exempt.

The department assesses applicants holistically; a competitive undergraduate GPA and demonstrated mathematical maturity are important. Some applicants without a mathematics major but with strong quantitative coursework and programming experience may be admitted, sometimes with recommended bridging courses.

Career prospects

Graduates of the Computational Mathematics master’s are well placed for careers that require strong quantitative and computational skills. Common career paths include:

• Computational scientist or numerical analyst in industry or government laboratories, working on simulation, modelling and algorithm development.
• Data scientist, machine learning engineer or quantitative analyst, applying statistical and numerical methods to large datasets.
• Software engineer focused on scientific or numerical software, high‑performance computing and algorithm implementation.
• Applied mathematician in engineering firms, finance, energy, or biomedical sectors tackling optimisation and modelling problems.
• Continuation to doctoral study (PhD) in applied mathematics, computational science, or related research fields.

Students also gain access to internship opportunities in the Kansas City region and partnerships with local industry and research organisations, which can provide practical experience and pathways into employment.

Why study at University of Missouri-Kansas City

UMKC’s Department of Mathematics and Statistics offers a focused environment with faculty research strengths in numerical analysis, optimisation, applied PDEs and computational methods. The university supports interdisciplinary collaboration with engineering, computer science and health sciences, enabling students to tailor their studies to applied problems.

Located in the heart of Kansas City, UMKC provides proximity to a diverse set of employers in technology, finance, biomedical research and government, creating opportunities for internships and applied projects. The campus maintains computing resources and facilities appropriate for numerical research, and graduate students may be eligible for assistantships that offer teaching or research experience alongside financial support.

Overall, the programme combines theoretical rigour with hands‑on computational training to prepare students for technically demanding careers or further research in computational mathematics and related disciplines.