University of North Carolina at Chapel Hill

What you'll study

The programme combines core statistical theory with applied methods commonly used in biomedical and public‑health research. Core topics typically include probability and statistical inference, linear models and regression, and applied computational statistics (including programming in R and SAS). Specialist modules cover generalized linear models, survival analysis, longitudinal and repeated measures analysis, categorical data analysis, clinical trials design and analysis, causal inference, and Bayesian methods.

Applied learning is emphasised through practicum components, collaborative projects with clinical or public‑health investigators, and a capstone or thesis option depending on the student’s pathway. Coursework also introduces data management, reproducible research practices, and consultations skills for working in multidisciplinary teams. Electives allow students to explore topics such as statistical genetics and bioinformatics, machine learning for health data, or advanced computational methods.

Entry requirements

Successful applicants typically hold a bachelor’s degree in statistics, mathematics, biostatistics, computer science, engineering or a closely related quantitative discipline. Admissions committees expect prior coursework in calculus (including multivariable calculus), linear algebra, and an introductory probability and statistics sequence. Programming experience (for example in R, Python or SAS) is highly recommended, as is evidence of quantitative problem‑solving ability.

Other components of a strong application include academic transcripts, letters of recommendation, and a personal statement that outlines research interests and relevant experience (such as internships, research projects or work in health‑data environments). International applicants should demonstrate English language proficiency where required. Specific credential and documentation requirements are listed by the School and can vary by pathway.

Career prospects

Graduates move into roles across academia, industry and the public sector. Common job titles include biostatistician, clinical trial statistician, data scientist, statistical programmer, and quantitative researcher. Employers include pharmaceutical and biotechnology companies, contract research organisations (CROs), public‑health agencies, hospitals and health systems, academic research centres, and technology firms working with health data.

The programme also prepares students for further study at doctoral level or for careers that require advanced methods in epidemiology, genomics, health economics and outcomes research. Practical experience gained through practica and collaborative projects enhances employability in multidisciplinary teams.

Why study at University of North Carolina at Chapel Hill

The programme is housed within UNC’s established schools and research communities that focus on public health and medicine, providing ready access to large, diverse datasets and cross‑disciplinary collaboration with clinicians, epidemiologists and basic scientists. Faculty include researchers active in clinical trials, epidemiology, genetics and computational methods, offering opportunities for mentorship and involvement in ongoing studies.

Students benefit from training that emphasises both methodological rigour and real‑world application, with opportunities to engage in practicum placements and collaborative research projects across the university and partner health organisations. The Chapel Hill environment, including the medical centre and public‑health agencies in the region, supports internships and employment networking for students pursuing careers in biostatistics and related fields.