University of Missouri-Kansas City

What you'll study

This programme combines rigorous mathematical theory with computational and applied techniques. Students study core topics such as advanced calculus and real analysis to build theoretical foundations, alongside applied and computational modules including numerical analysis, partial differential equations, optimisation, and mathematical modelling. Probabilistic and statistical methods, dynamical systems, and data analysis are typically included to prepare students for interdisciplinary work.

• Core subjects: real and complex analysis, linear and functional analysis, numerical methods, and differential equations.
• Applied modules: mathematical modelling, finite element and finite difference methods, optimisation and control, stochastic processes and applied probability.
• Computational skills: scientific computing, programming for mathematics (e.g. Python, MATLAB), high-performance numerical methods and data-driven techniques.
• Research/training components: applied mathematics seminar, a substantial master’s thesis or an applied project option that involves modelling, computation and written/oral defence.

The programme is offered with thesis and non-thesis tracks; students choose a path that emphasises research (thesis) or professional application (project/coursework). Typical study pathways include a mixture of core and elective courses, culminating in a research project or technical report guided by faculty advisers.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related quantitative discipline. Successful candidates typically have prior coursework in multivariable calculus, linear algebra, ordinary differential equations and introductory real analysis. Familiarity with programming and numerical methods is advantageous.

• Academic transcript: evidence of a strong undergraduate record in quantitative subjects.
• Supporting documents: statement of purpose outlining research or career goals, curriculum vitae, and one or more letters of recommendation from academic or professional referees.
• For international applicants: proof of English proficiency through recognised tests unless exempt; additional documentation may be required for visa and enrolment purposes.
• Additional considerations: some applicants may be invited to discuss their preparation with faculty; applicants without a mathematics degree but with substantial quantitative preparation may be admitted with preparatory coursework.

Career prospects

Graduates move into a wide range of careers that require strong analytical, modelling and computational skills. Common destinations include quantitative analyst and modelling roles in finance, data scientist and machine learning engineer positions in technology firms, and numerical analyst or simulation specialist jobs in engineering and energy companies. Applied mathematicians are also employed in aerospace, defence, telecommunications, and life sciences for tasks such as optimisation, forecasting, risk analysis and computational biology.

Many graduates progress to doctoral study in mathematics, applied mathematics or related interdisciplinary fields when they pursue research careers in academia, national laboratories or industrial research centres. The programme’s balance of theory and computation prepares students for both immediate professional roles and further research training.

Why study at University of Missouri-Kansas City

UMKC’s Department of Mathematics and Statistics offers a focused applied mathematics programme with access to faculty who work on numerical analysis, applied probability, dynamical systems and computational modelling. The university’s urban location in the Kansas City metropolitan area provides opportunities for collaborative projects and internships with local industry, healthcare systems and technology companies.

• Faculty and research: students benefit from supervision by faculty active in applied and computational research, and from seminar series that connect students with current problems and methods.
• Computational resources: access to departmental computing facilities and software commonly used in industry and research supports hands-on training in large-scale computation and simulation.
• Industry links and internships: connections with regional employers allow applied project work and internship opportunities that bridge academic learning and professional experience.
• Student support: small cohort sizes and departmental advising help tailor the programme to individual career goals, whether industry-focused or research-oriented.

Overall, UMKC’s applied mathematics master’s emphasises practical, computationally grounded training delivered in a setting where students can quickly engage with applied research and local industry partners.