Columbia University

What you'll study

The programme combines core statistical theory and practical applied biostatistics. Core topics typically include probability theory, statistical inference, linear models and regression, and computational statistics. Biostatistics-focused modules often cover survival analysis, longitudinal data analysis, causal inference, categorical data methods and study design for clinical trials.

Students also encounter modern topics such as Bayesian methods, high-dimensional and functional data analysis, statistical genomics and machine learning for biological data. Training emphasises computation and reproducibility, with coursework in statistical programming (e.g. R and Python), simulation methods and software development for data analysis.

Learning formats include lectures, problem sets, laboratory sessions, and a substantial applied component. Many students complete a practicum or capstone project in collaboration with faculty in Columbia's Department of Statistics, the Mailman School of Public Health, the medical centre or an external partner. Research-led electives and opportunities to work on faculty research projects allow deeper specialization.

Entry requirements

Applicants are expected to hold a bachelor's degree (or equivalent) with a strong quantitative background, typically in statistics, mathematics, computer science, engineering, or a closely related discipline. Successful candidates have demonstrated competence in calculus, linear algebra, probability and introductory statistics, and practical experience with programming or data analysis is highly desirable.

Application materials generally include official academic transcripts, a personal statement describing quantitative preparation and research or professional goals, and letters of recommendation. International applicants usually must provide evidence of English language proficiency. Standardised test requirements (such as the GRE) and other specifics vary by programme and should be checked on the university's admissions pages.

Career prospects

Graduates enter a broad range of roles across industry, public sector and academia. Typical positions include biostatistician or statistical programmer in pharmaceutical and biotechnology companies, data scientist or analyst in healthcare organisations, epidemiologist-statistician in public health agencies, and research statistician in academic medical centres.

Alumni also find roles at contract research organisations (CROs), diagnostic and genomic companies, health-tech start-ups and non-profit research organisations. The degree also provides a strong foundation for those who wish to pursue a PhD in biostatistics, statistics or related disciplines.

Why study at Columbia University

Columbia offers access to a dense ecosystem of strengths in public health, medicine and quantitative research. Students benefit from close links between the Department of Statistics and the Mailman School of Public Health, opportunities to collaborate with clinical researchers at Columbia University Irving Medical Center, and access to cross-disciplinary centres and facilities focused on genomics, computational biology and population health.

Being located in New York City gives students proximity to a large and active biotech and health-data industry, which supports internships, practicum placements and industry collaborations. Faculty teaching on the programme include researchers active in methodological and applied biostatistics, providing mentoring and pathways into research projects. The programme’s combination of theoretical training, applied experience and institutional connections prepares graduates for both practitioner roles and further academic study.