Georgia Institute of Technology

What you'll study

The programme builds a strong foundation in probability, statistical inference and computational methods, with applied coursework targeted to biomedical problems. Core topics typically include probability theory, statistical inference, linear models and regression, and statistical computing. Electives and specialised modules relevant to biostatistics often cover generalized linear models, survival analysis, longitudinal and correlated data, categorical data analysis, Bayesian methods, statistical learning, high‑dimensional data analysis, and design and analysis of clinical trials.

Students can expect a mix of theoretical classes, applied labs and project work. Practical components often involve statistical computing in R and Python, hands‑on analysis of biomedical datasets, consulting or practicum experiences with research groups, and an option between a research thesis or a practitioner’s report depending on the chosen degree pathway.

Entry requirements

• Bachelor’s degree in statistics, mathematics, computer science, engineering, or a closely related quantitative discipline.
• Strong preparation in calculus, linear algebra, probability and mathematical statistics. Coursework in programming or data analysis (R, Python or equivalent) is expected.
• Academic transcripts demonstrating rigorous quantitative training; competitive applicants typically have a strong overall academic record.
• Letters of recommendation (usually two or three) from academic or professional referees who can speak to quantitative ability and research or applied potential.
• A statement of purpose outlining research interests, experience with statistical or computational projects, and career goals—particularly how biostatistics fits those objectives.
• For applicants whose first language is not English, an approved English proficiency test score is required unless exempted under institutional policy.
• Applicants with limited formal statistical background may be admitted conditionally and placed on a preparatory course sequence; prior coursework and programming experience strengthen an application.

Career prospects

Graduates are prepared for roles as biostatisticians and quantitative scientists across academia, industry and the public sector. Typical positions include biostatistician or statistical programmer in pharmaceutical and biotech companies, quantitative analyst for clinical trials and regulatory submissions, epidemiological data analyst in public‑health agencies, research statistician in medical research institutes, and data scientist roles in health‑care analytics.

Alumni also pursue doctoral study in statistics, biostatistics, biomedicine or related fields. The programme’s emphasis on computing and applied collaboration makes graduates competitive for roles that require translating complex biomedical data into actionable scientific or clinical insight.

Why study at Georgia Institute of Technology

• Interdisciplinary environment: Georgia Tech’s strengths in statistics, computing and engineering provide rich opportunities to work on biomedical problems with researchers across campus and with clinical and public‑health partners in Atlanta.
• Computational resources and training: the department emphasises modern statistical computing, reproducible research and access to high‑performance computing facilities important for large biomedical datasets.
• Research and collaboration opportunities: students can engage in applied projects and collaborations involving genomics, clinical trials, epidemiology, and health‑data analytics through faculty labs and cross‑institution partnerships.
• Career support and industry links: proximity to major hospitals, research institutes and a growing biotech sector in Atlanta, combined with Georgia Tech’s career services and strong employer connections, helps graduates transition into industry, government and academic roles.
• Experienced faculty: instruction and supervision from faculty active in methodological and applied biostatistics, offering mentorship for both applied projects and research pathways.