University of Michigan

What you'll study

This professionally focused master’s emphasises core statistical theory alongside intensive training in computing and applied data analysis. Students typically complete a mix of required core courses and electives, culminating in a capstone project, practicum or applied consulting experience with an external client or interdisciplinary research group.

• Core subjects: probability and mathematical statistics, statistical inference, linear models and regression, experimental design.
• Computing and methods: statistical computing and data management (R/Python), resampling and simulation, Bayesian methods, machine learning and regularisation, survival analysis and time‑series.
• Advanced electives: multivariate analysis, causal inference, high‑dimensional statistics, nonparametric methods, spatial statistics, biostatistical methods (available through cross‑listing with public health), data visualisation and interactive reporting.
• Capstone / practicum: applied team projects or practicum placements that emphasise reproducible analysis, communication of results to non‑technical audiences and deployment of statistical models.

Programme structure is primarily coursework based, with many students combining classroom learning with hands‑on projects using real datasets from industry, campus research centres or public repositories. Electives let students tailor training toward domains such as health, social science, engineering or finance.

Entry requirements

Applicants should hold a bachelor’s degree (or equivalent) with substantial quantitative content. Typical preparation includes coursework in calculus, linear algebra, probability and introductory statistics; familiarity with mathematical proof is advantageous. Programming experience in R, Python, or another scientific language and exposure to data structures or algorithms strengthen an application.

Applications usually require academic transcripts, a statement of purpose describing quantitative background and goals, a CV, and letters of recommendation. Some applicants also submit GRE scores if they have them; specific test requirements vary, so check the department’s admissions guidance. Competitive applicants demonstrate strong quantitative grades, evidence of analytic problem‑solving and clear motivation for applied statistics.

Career prospects

Graduates enter a wide range of roles across sectors. Common entry positions include statistician, data scientist, data analyst, quantitative analyst, biostatistician, research analyst and analytics consultant. Employers span technology and software firms, healthcare and pharmaceutical companies, finance and insurance, government agencies, research institutes and management consultancies.

The programme’s applied focus and practicum projects are intended to prepare students for data‑centred work that emphasises model building, uncertainty quantification, experimental design and communication of results to stakeholders. Many graduates also use the master’s as preparation for doctoral study in statistics, biostatistics or related quantitative fields.

Why study at University of Michigan

The University of Michigan offers a strong, research‑informed statistics environment with broad interdisciplinary connections across public health, engineering, social sciences, business and medicine. Students benefit from access to experienced faculty, computational resources and several campus research centres that foster collaborative applied projects.

Being based in Ann Arbor provides a vibrant academic community and links to nearby industry and healthcare partners, supporting practicum placements and internships. The department’s emphasis on practical training, combined with a large alumni network and career services on campus, helps graduates move quickly into professional roles or further graduate study.