Michigan Technological University

What you'll study

The programme combines core theoretical statistics with applied biostatistical methods and computational training. Typical core topics include probability and statistical inference, linear models and regression, generalized linear models, and statistical computing (with emphasis on R and other scientific computing tools). Biostatistics-focused modules commonly cover survival analysis, longitudinal and repeated-measures methods, categorical data analysis, design and analysis of clinical trials, and Bayesian methods for biomedical data.

Students also encounter supporting subjects such as experimental design, multivariate analysis, statistical learning and machine learning techniques, and data management for health research. Research seminars and journal clubs give exposure to current problems in biostatistics, and students may choose between a thesis track (research-focused) or a project/coursework track that includes an applied consulting or capstone project with real biomedical data.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, statistics, biostatistics, a quantitative science, engineering, or a closely related field. Typical preparation includes coursework in calculus, linear algebra, probability, and at least one introductory statistics course. Programming experience (for example in R, Python, or another statistical language) is highly recommended.

Admission materials generally include academic transcripts, a statement of purpose explaining preparation and goals in biostatistics, a curriculum vitae or résumé, and letters of recommendation. International applicants must demonstrate English language proficiency through recognised tests or equivalent documentation. Additional requirements or expectations—such as minimum GPA thresholds or the consideration of standardised test scores—are outlined by the university’s graduate admissions office and may vary by application cycle.

Career prospects

Graduates with a master’s in statistics concentrating in biostatistics find roles across the healthcare and life sciences sectors. Typical job titles include biostatistician, data analyst for clinical research, statistical programmer, epidemiologist (with further training), statistical consultant, and data scientist in pharmaceutical companies, contract research organisations (CROs), hospitals, public health agencies, and medical device firms.

The programme also prepares students for continued study toward a PhD in biostatistics, statistics, epidemiology, or other quantitative biomedical research fields. Practical training in study design, regulatory considerations for clinical trials, and modern computational methods makes graduates attractive for both applied and research-oriented positions.

Why study at Michigan Technological University

Michigan Technological University offers a strong quantitative environment with small cohort sizes that enable close mentorship by faculty and opportunities for collaborative, interdisciplinary research. The Department of Mathematical Sciences places emphasis on applied statistics and computational skills, giving students hands‑on experience with real datasets and statistical software used in biomedical research.

Students benefit from cross-campus collaborations with researchers in health sciences, engineering and environmental health, access to high‑performance computing resources, and opportunities for internship or practicum placements with regional and national partners. The department’s emphasis on both theory and practice helps graduates develop the technical and communication skills needed to translate statistical results into actionable insight for biomedical and public‑health decision makers.