Grand Valley State University

What you'll study

The programme combines core statistical theory with specialised biostatistical methods and computing. Core modules typically cover probability and statistical inference, linear models and regression, and statistical computing with R. Biostatistics‑specific topics include survival analysis, longitudinal data analysis, design and analysis of clinical trials, categorical data methods, and epidemiologic methods. Students can expect coursework in Bayesian methods and advanced regression, plus applied modules in data management, reproducible research, and high‑dimensional data analysis.

Students normally choose between a thesis or a project/capstone option. The thesis route emphasises original research supervised by faculty; the project/capstone route emphasises applied analysis for an external partner or a faculty‑led collaborative study. Practical experience is reinforced through lab classes, a consulting practicum, and opportunities to work with local healthcare providers, public‑health agencies or industry partners on real datasets.

Entry requirements

Applicants are expected to hold a bachelor’s degree in statistics, mathematics, biostatistics, computer science, or a related quantitative discipline. Typical preparation includes coursework in calculus (including multivariable calculus), linear algebra, introductory probability and mathematical statistics, and at least one programming or computing course (for example R, Python or SAS).

• Academic transcript(s) demonstrating relevant undergraduate preparation.
• A personal statement outlining quantitative background, interests in biostatistics and career goals.
• Letters of recommendation, usually two or three, from academic or professional referees familiar with the applicant’s quantitative work.
• A curriculum vitae or résumé detailing relevant coursework, research, or work experience.
• International applicants must meet English language proficiency requirements through recognised tests or prior study in English.

Standardised tests (such as the GRE) may be optional or considered on a case‑by‑case basis; applicants should consult the programme’s admissions guidance for current testing policies. Applicants with limited formal preparation may be admitted conditionally and asked to complete prescribed bridging coursework.

Career prospects

Graduates are prepared for roles as biostatisticians or statistical analysts in pharmaceutical and biotech companies, clinical research organisations, hospitals and health systems, public‑health agencies, and academic research centres. Typical job titles include Biostatistician, Clinical Data Analyst, Statistical Programmer, Epidemiologic Analyst, and Research Scientist. The programme also provides a strong foundation for students who wish to pursue doctoral study in biostatistics, statistics or epidemiology.

Skills emphasised—statistical modelling, survival and longitudinal methods, clinical‑trial design, data wrangling and reproducible reporting—are in demand for roles supporting clinical research, regulatory submissions, outcomes research and health analytics. Internship and practicum connections with regional healthcare providers and industry partners help graduates transition into applied positions.

Why study at Grand Valley State University

Grand Valley State University offers a supportive, applied learning environment with small class sizes and access to faculty who engage in collaborative research across health sciences and public health. The Department of Mathematics & Statistics emphasises hands‑on training in modern statistical computing and reproducible workflows, preparing students for practical challenges in biomedical and public‑health data.

Located in West Michigan, the university has established links with regional hospitals, research organisations and industry, providing opportunities for practicum placements and applied projects. Students benefit from computing resources, statistical consulting experience, and a community that supports professional development and preparation for both industry roles and doctoral study.