University of Virginia

What you'll study

The programme builds a core grounding in probability, mathematical statistics and statistical thinking, then moves into applied biostatistical methods and computing. Core topics typically include probability theory, mathematical statistics, linear models and regression, and statistical inference. Applied and advanced modules often cover regression diagnostics, generalized linear models, survival analysis, longitudinal data analysis, and experimental design.

Computing and data skills are emphasised through coursework in statistical programming (R and Python), data management, reproducible research and simulation. Students take laboratory‑style classes and projects that use real biological and clinical data. Electives and cross‑departmental options let you study introductory epidemiology, genetics/genomics, bioinformatics, computational biology, and machine learning.

Programme structure normally combines foundation courses, a sequence of intermediate and advanced statistics/biostatistics modules, supporting mathematical prerequisites (calculus and linear algebra), and free electives. Many students complete a capstone or honours project in collaboration with faculty in the Statistics Department, the School of Medicine or the School of Public Health, and opportunities for undergraduate research and internships with UVA Health and regional biotech are available.

Entry requirements

Admissions expect strong performance in mathematics; successful applicants typically have substantial prior experience with calculus and quantitative reasoning. For students from A‑level or similar systems, competitive preparation includes Advanced Level mathematics or equivalent; for international and IB applicants, higher‑level mathematics is strongly recommended. US applicants generally present strong high‑school grades with coursework in calculus and statistics where available.

Admissions take a holistic view: personal statements, teacher references, and evidence of quantitative interest (such as coursework, extracurricular projects, or programming experience) are important. Applicants without extensive biology background can still apply but should be prepared to take introductory life‑science modules if they wish to pursue biostatistics electives.

Career prospects

Graduates are well placed for roles that apply quantitative methods to biomedical and public‑health problems. Typical entry positions include junior biostatistician or data analyst in pharmaceutical companies, contract research organisations, hospitals and public‑health agencies, clinical‑trial support roles, and roles in health‑tech start‑ups. Employers also recruit graduates into broader data‑science and analytics positions across industry and government.

Many students progress to postgraduate training (master’s or PhD) in biostatistics, statistics, epidemiology or data science, which is a common route for those aiming for independent research or senior statistical roles in academia, clinical research or regulatory science.

Why study at University of Virginia

The University of Virginia offers a statistics curriculum with strong ties to its medical and public‑health schools, providing practical exposure to clinical and translational research. Students benefit from faculty who are active in biostatistical research and collaborations across biomedical disciplines, and from access to research centres and clinical data through UVA Health.

Undergraduates have opportunities for supervised research, internships with local biotech and healthcare partners in the Charlottesville region, and interdisciplinary study with departments such as Biology, Computer Science and Public Health. The university’s career services and alumni network support placement into industry and postgraduate programmes, while small seminar classes and capstone projects provide hands‑on training in applied biostatistics.