University of Missouri-Kansas City

What you'll study

The programme builds a firm foundation in pure and applied mathematics alongside core statistical theory and methods. Early years emphasise calculus, linear algebra and introductory probability and statistics. Progressing modules introduce real analysis, abstract algebra, differential equations and numerical methods, while statistics modules cover statistical inference, regression, experimental design and computational statistics.

• Core mathematics: Calculus sequence, Linear Algebra, Differential Equations, Real Analysis, Abstract Algebra, Mathematical Modelling.
• Core statistics: Probability Theory, Mathematical Statistics, Regression and ANOVA, Design of Experiments, Time Series, Multivariate Methods.
• Computational and applied options: Numerical Analysis, Scientific Computing, Statistical Computing (R/Python), Data Mining and Machine Learning electives.
• Capstone and project work: Upper-level capstone project or honours thesis involving original problem solving, data analysis or modelling, often with an applied focus or industry partner.
• Electives and interdisciplinary study: Applied electives across economics, computer science, engineering, biology or finance to tailor the degree toward actuarial, data science or research pathways.

Assessment typically combines problem sets, programming assignments, exams and a final project or research component in the senior year.

Entry requirements

Applicants are expected to have a strong secondary-school background in mathematics. Typical preparation includes completion of algebra, geometry, trigonometry and preferably one or two years of college-preparatory mathematics such as pre-calculus or calculus. Strength in logical reasoning and quantitative problem solving is essential.

• Academic qualifications: High-school diploma or equivalent with strong performance in mathematics. Applicants transferring from other colleges should have completed introductory calculus.
• Standardised tests and additional evidence: The university's standard admissions processes apply; standardised test requirements may vary and some applicants submit ACT or SAT scores. Admission decisions consider the overall academic record and the strength of mathematics coursework.
• International applicants: Proof of English language proficiency is required through accepted tests or prior education in English; equivalent academic credentials are assessed for admission suitability.
• Preparatory routes: Students who need strengthening in calculus or statistics can often take preparatory courses before progressing to upper-division modules.

Career prospects

Graduates leave with mathematical rigour and practical statistical skills valued across many sectors. The degree prepares students for technical, analytical and research roles, and provides a strong platform for graduate study in mathematics, statistics, data science, actuarial science or related disciplines.

• Data analyst, data scientist or statistician in technology, healthcare, public policy, or consulting.
• Actuarial roles in insurance and finance (with professional exam preparation).
• Quantitative analyst roles in banking and investment firms.
• Software developer or algorithm engineer, especially where numerical methods and modelling are required.
• Secondary school or community college teaching (with appropriate certification), and academic research following postgraduate study.
• Positions in government agencies, research institutes and non-profit organisations that require strong quantitative and modelling skills.

Why study at University of Missouri-Kansas City

UMKC offers a mathematics and statistics programme with close faculty contact, opportunities for undergraduate research and access to computing and statistical software used in professional practice. The department supports applied projects and internships, leveraging Kansas City’s diverse economy and ties with healthcare, finance and technology employers.

• Faculty and research opportunities: Access to faculty active in both pure and applied research, and opportunities to participate in supervised research or collaborative projects.
• Practical training: Computing labs and coursework that emphasise programming with tools such as R and Python, and hands-on data analysis experience.
• Interdisciplinary links: Strong links with computing, engineering, business and health disciplines allow students to tailor the degree for specific careers.
• Career support: Internship and career services in Kansas City help students find placements with local employers and prepare for professional exams or graduate study.