University of Virginia

What you'll study

This programme combines core statistical theory with specialised coursework and applied training in biostatistics. Core topics typically include probability and statistical inference, linear and generalized linear models, and statistical computing. Biostatistics‑focused modules cover survival analysis, longitudinal data analysis, clinical trials design and analysis, causal inference, and statistical genetics or bioinformatics.

Students learn practical skills in statistical programming (commonly R and Python), data management, reproducible workflows, and modern computational methods such as Bayesian modelling and mixed‑effects models. The programme offers an applied practicum or capstone project working with researchers in the School of Medicine, public‑health units or affiliated health systems, and some students choose a thesis option under faculty supervision.

Typical structure

• Core coursework in probability, inference and regression methods
• Specialist biostatistics modules such as survival analysis and clinical trials
• Computational and data‑analysis training with an emphasis on reproducible research
• An applied practicum, capstone project or research thesis involving real biomedical data

Entry requirements

Applicants should normally hold a bachelor’s degree in statistics, mathematics, biostatistics, computer science, or a closely related quantitative discipline. Admissions selectors look for preparation in calculus, linear algebra, and introductory probability and statistics. Prior experience with statistical computing (for example in R or Python) and coursework in mathematical statistics or probability is advantageous.

Typical application materials include a transcript, a personal statement describing research and career aims, academic or professional references, and a curriculum vitae. International applicants must demonstrate English language proficiency when required by the university. Standardised tests may be optional or considered on a case‑by‑case basis—check the department’s admissions guidance for current policy.

Career prospects

Graduates are prepared for roles as biostatisticians or statistical programmers in pharmaceutical and biotech companies, contract research organisations (CROs), academic research groups, hospitals and public‑health agencies. Common positions include clinical‑trials statistician, epidemiological data analyst, biometrics/statistical scientist, and roles in precision medicine or genomics data analysis. The combination of methodological training and applied practicum experience also provides a pathway to doctoral study in biostatistics or related fields.

Why study at University of Virginia

Studying at the University of Virginia gives access to close collaboration with the School of Medicine and the university’s affiliated health system, enabling practicum placements and research on contemporary biomedical problems. The statistics and public‑health communities at UVA emphasise interdisciplinary work, offering opportunities to work alongside clinicians, epidemiologists and genetics researchers.

Students benefit from faculty expertise across theoretical and applied biostatistics, modern computational resources and a supportive academic environment in Charlottesville. The programme is designed to balance rigorous statistical training with real‑world experience so graduates can step directly into research, industry or clinical trial settings.