University of Michigan

What you'll study

The programme builds a foundation in calculus, linear algebra and probability, then progresses to core statistical topics such as mathematical statistics, regression, statistical inference and experimental design. From there, students follow biostatistics-focused coursework and electives that emphasise the analysis of biological and health data.

• Core mathematics and probability: single- and multivariable calculus, linear algebra, and probability theory that underpin statistical methods.
• Fundamental statistics: mathematical statistics, estimation and hypothesis testing, linear and generalized linear models, and regression diagnostics.
• Computing and data analysis: statistical programming (R and/or Python), simulation, reproducible research, and software tools for large datasets.
• Biostatistics and applied health modules: design and analysis of clinical trials, survival analysis, longitudinal data analysis, causal inference, epidemiologic methods, and introductory bioinformatics/statistical genetics.
• Practical experience: data analysis projects, lab or practicum placements with faculty in public health or medicine, and opportunities for a senior capstone project or honours thesis.
• Electives and interdisciplinary options: courses in molecular biology, epidemiology, public health, computer science, and domain-specific electives to tailor the programme to interests such as genomics or health services research.

Structure

Students typically complete a sequence of prerequisite mathematics and introductory statistics in the first two years, core statistical theory and applied methods in the middle years, and specialised biostatistics electives plus a capstone or practicum in the final year. The programme encourages research assistantships and internships with faculty in the Department of Statistics, the School of Public Health, and Michigan Medicine for hands-on experience.

Entry requirements

Admission to the University of Michigan is handled by the university; prospective students for the statistics major are expected to demonstrate strong preparation in high school mathematics—ideally including calculus—and good achievement in sciences and computing. Successful applicants typically show evidence of quantitative ability, problem-solving skills and experience with programming or data analysis where possible.

• Academic preparation: high-school coursework in advanced mathematics (calculus), science, and writing.
• Recommended skills: familiarity with basic programming concepts or experience in languages such as R, Python or MATLAB is advantageous.
• Progression into the major: some students enter directly as statistics majors while others transfer into the major after completing prerequisite courses and meeting departmental requirements; consult departmental advising for specifics.

Career prospects

Graduates with a statistics degree focused on biostatistics are well placed for quantitative roles in health-related sectors and for further graduate study. Common career paths include clinical trials statistician, biostatistician in academia or industry, epidemiological data analyst, health data scientist, statistical programmer in pharmaceutical companies, and roles in regulatory agencies and public health organisations.

• Industry: pharmaceutical and biotechnology companies, medical device firms, health-tech start-ups.
• Public sector and healthcare: hospitals, public health agencies, research hospitals and government health departments.
• Research and academia: research assistantships, preparation for master’s or PhD programmes in biostatistics, statistics, epidemiology or related fields.
• Data science and analytics: broader data scientist or quantitative analyst roles that leverage statistical modelling, machine learning and domain knowledge in life sciences.

Why study at University of Michigan

The University of Michigan offers a strong combination of theoretical training and applied opportunities in biostatistics. Students benefit from collaboration across the Department of Statistics, the School of Public Health and Michigan Medicine, gaining access to active research projects, clinical datasets and internship pathways. The departmental curriculum emphasises computing and reproducible research, while faculty-led research and multidisciplinary centres create chances for undergraduates to participate in real-world health-data work.

Advising, student organisations and seminar series further support professional development and connections with employers and graduate programmes in the life sciences, public health and industry.