Graduate Statistics
The University of Virginia’s graduate programs in Statistics provide rigorous quantitative training grounded in both theoretical and applied statistical methods. The Master of Science (M.S.) in Statistics is a flexible, three‑semester program requiring 30 credits of graded coursework across statistical theory, linear models, consulting, and a wide range of electives. Students may pursue concentrations in Data Analytic Methods or Biostatistics, and must complete exams demonstrating computing, data analysis, and presentation competencies. The program prepares students for applied statistics and data‑focused roles in industry, government, and health sciences.
The Ph.D. in Statistics offers advanced research‑focused training with wide elective breadth and opportunities to integrate coursework from allied fields such as biostatistics, mathematics, engineering, computer science, or economics. Designed for students pursuing careers in academia, applied research, quantitative consulting, or government and industry analytics, the Ph.D. emphasizes methodological depth, interdisciplinary flexibility, and original research guided by faculty mentors. Both degrees benefit from close collaboration with UVA’s Division of Biostatistics and Epidemiology.
What Can I Do With This Degree?
- Applied Statistician or Data Analyst in industry or government settings
- Biostatistician in health, clinical, biomedical, or public‑health research environments
- Quantitative Researcher or Methodologist in tech, finance, or research firms
- Statistical Consultant working across business, science, or policy sectors
- University or College Professor specializing in statistics or data science (Ph.D.)
- Research Scientist in interdisciplinary fields that combine statistics with economics, engineering, computer science, or biostatistics
- Survey Statistician or Government Analyst (e.g., census, labor, health, environmental agencies)
- Machine Learning or AI Specialist using advanced statistical modeling
- Applied and Theoretical Statistics, including linear models, inference, and mathematical statistics
- Data Analytics and Machine Learning, including data mining, statistical computing, Bayesian methods, and causal inference
- Biostatistics, including survival analysis, longitudinal data analysis, clinical trials, and health‑related statistical modeling
- Time Series, Multivariate, and Categorical Data Analysis
- Optimization, Monte Carlo, and Computational Statistics
- Survey Sampling and Experimental Design
- Interdisciplinary Statistical Research integrating economics, engineering, computer science, mathematics, or biostatistics (supported through Ph.D. electives)