University of Cincinnati

What you'll study

The programme combines core statistical theory with applied biostatistical methods and computing. Typical topics include: probability and statistical inference, linear models and regression, generalized linear models, survival analysis, longitudinal data analysis, categorical data methods, and design and analysis of clinical trials.

Students also take courses in statistical computing and data management (R, SAS, reproducible workflows), Bayesian methods, and specialized electives such as statistical genetics, high-dimensional data analysis, or epidemiologic methods. Most students undertake a practicum or capstone project that involves collaborating with clinical researchers or public-health practitioners; a thesis option may be available for those who plan to continue to doctoral study.

The programme is normally completed through a combination of required core courses and electives, with a balance of theory and applied computing. Full-time students typically finish in one to two years depending on course load and the choice of a thesis or practicum.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution. Strong preparation in calculus, linear algebra and introductory probability and statistics is required; undergraduate coursework in programming or data analysis is highly desirable. Degrees in mathematics, statistics, biostatistics, economics, engineering, computer science or related quantitative sciences are typical among applicants.

Admissions materials generally include official transcripts, a personal statement describing research or professional goals, a curriculum vitae or résumé, and letters of recommendation. International applicants must demonstrate English language proficiency according to the university’s standard requirements. Specific departmental prerequisites and documentation requirements can be confirmed via the department admissions pages.

Career prospects

Graduates enter a wide range of roles across health-related sectors. Common job titles include biostatistician, statistical programmer, clinical data analyst, data scientist and statistical consultant. Employers include academic medical centres, pharmaceutical and biotechnology companies, contract research organisations (CROs), public-health agencies, medical device firms and health technology start-ups.

Alumni pursue further study at the doctoral level or move directly into research and regulatory-facing positions where they design and analyse clinical trials, contribute to observational studies and registries, develop predictive models for healthcare outcomes, and support translational research across interdisciplinary teams.

Why study at University of Cincinnati

The University of Cincinnati offers close connections between the Department of Mathematical Sciences and the medical and public-health research community, providing strong opportunities for applied projects and internships. Students benefit from collaborative links with the College of Medicine, affiliated hospitals and regional healthcare organisations, which support practicum placements and real-world datasets.

Resources such as computing facilities, statistical consulting centres and a strong co-operative education ethos give students practical experience valued by employers. Faculty members are actively engaged in methodological and applied research in biostatistics, providing mentorship for applied projects and pathways to further research training.