University of Missouri-Kansas City

What you'll study

The programme offers a blend of core theory, applied topics and elective specialisations. Students typically follow either a thesis or non-thesis track. Core coursework commonly covers real analysis, linear algebra at an advanced level, probability theory, and mathematical statistics. From there you can choose electives in areas such as applied and computational mathematics, numerical analysis, stochastic processes, multivariate analysis, regression and time series, statistical learning, and optimisation.

Programme components often include:

• Core theoretical courses — graduate real analysis, measure-theoretic probability, and algebraic topics that build proof and abstraction skills.
• Statistical methodology — courses in estimation, hypothesis testing, linear models, generalized linear models, and nonparametric methods.
• Computational and applied modules — numerical methods, scientific computing, statistical computing with modern software, and data analysis practicum.
• Research and seminar — reading seminars, supervised research leading to a thesis for those on the research track, or a project-based capstone for the professional option.

Students are exposed to both theory and practical data-work, with access to computing facilities and opportunities to work with faculty on research projects in pure mathematics, applied mathematics and statistical modelling.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, statistics or a closely related field. Strong preparation in calculus (through multivariable), linear algebra, and introductory proof-based mathematics is expected. Typical application materials include official academic transcripts, a statement of purpose, and letters of recommendation. International applicants must demonstrate English proficiency through an accepted test unless exempt.

Where undergraduate preparation is incomplete, applicants may be admitted conditionally and asked to take specified undergraduate-level courses before advancing to full graduate standing. The department reviews applications for mathematical maturity, quantitative background and fit with faculty interests.

Career prospects

Graduates pursue a range of careers across industry, government and academia. Common pathways include:

• Data scientist or analyst roles in finance, healthcare, technology and logistics, where statistical modelling and computational skills are applied to real-world data.
• Actuarial positions or roles in risk analysis that leverage probability and statistical techniques; graduates often prepare for professional actuarial exams alongside the degree.
• Research and development roles in engineering and biotech that require applied mathematics and numerical methods.
• Progression to doctoral study in mathematics, statistics or related disciplines for those pursuing academic or high-level research careers.
• Teaching and curriculum development positions at the secondary and collegiate level for those combining the degree with pedagogical credentials.

Internships and collaborations with regional employers in the Kansas City area provide practical experience and local industry contacts.

Why study at University of Missouri-Kansas City

UMKC offers a graduate environment with relatively small class sizes and direct access to faculty, which supports close mentorship and research collaboration. The department maintains a balance of pure and applied research expertise, allowing students to tailor programmes toward theoretical mathematics or practical statistical applications. Located in a metropolitan region with a diverse business community, UMKC provides opportunities for internships and partnerships with industry, healthcare institutions and government agencies.

Graduate assistantships and teaching opportunities are available to support professional development and offset costs. The university’s computing resources, regular seminars and a collegial departmental culture help students build the technical skills and professional network needed for careers in academia, industry and the public sector.