Columbia University

What you'll study

The undergraduate statistics major at Columbia builds a foundation in calculus, probability and linear algebra before advancing to core topics in statistical inference, regression, experimental design and statistical computing. Students concentrating in biostatistics select courses that apply these methods to biological and medical data, such as survival analysis, longitudinal data analysis, categorical data methods, causal inference and high‑dimensional data techniques.

Typical modules and topics you can expect include:

• Foundations: single and multivariable calculus, linear algebra, introductory probability.
• Core statistics: probability theory, mathematical statistics, point and interval estimation, hypothesis testing.
• Applied methods: linear and generalized linear models, regression diagnostics, design of experiments, mixed‑effects models.
• Biostatistics specialisms: survival analysis, longitudinal and repeated measures, categorical data analysis, clinical trials methodology, biometrics and introductory bioinformatics.
• Computing and data science: statistical computing (R and Python), simulation, reproducible research, data management and working with large biological datasets.
• Electives and cross‑disciplinary options: courses at the Columbia Mailman School of Public Health, molecular biology or genetics electives, epidemiology, and seminars in genomics or health data science.

Undergraduates are encouraged to undertake a capstone project, directed reading, or research with faculty in the Department of Statistics or in collaborative groups across Columbia’s medical and public health schools. Practical experience through internships in hospitals, pharmaceutical firms, public‑health agencies and biotech labs in New York complements classroom learning.

Entry requirements

Admission to Columbia is highly selective and assessed holistically. For the statistics major specialising in biostatistics, successful applicants typically demonstrate strong preparation in mathematics (calculus and preferably multivariable calculus and linear algebra) and evidence of quantitative aptitude. Prior exposure to programming or data analysis (for example through coursework, projects or self‑study in R, Python or similar) is advantageous.

Typical application materials include academic transcripts, standardised test information where applicable, teacher or school recommendations, and a personal statement that explains your interest in statistics and its application to biology or health. Transfer students should show college‑level strength in calculus and introductory statistics; prospective students with international qualifications should consult Columbia’s admissions pages for equivalent requirements.

Career prospects

Graduates with a statistics major and biostatistics focus enter a range of professional paths. Common roles include biostatistician or statistical programmer in pharmaceutical and biotechnology companies, data analyst or research analyst in hospitals and health systems, clinical trial statistician, public‑health analyst in government or non‑profit organisations, and bioinformatics or computational biology positions.

Many graduates also pursue further study — master’s or doctoral programmes in biostatistics, statistics, epidemiology or related fields — to prepare for advanced research or academic careers. The programme’s emphasis on computing and applied methods also equips students for broader data‑science roles across industries where biological or health data are central.

Why study at Columbia University

Columbia offers a distinctive combination of rigorous statistical training and immediate access to world‑class biomedical and public‑health resources. The department’s faculty are active in both theoretical and applied research, and undergraduates benefit from opportunities to collaborate with researchers at the Columbia University Irving Medical Center and the Mailman School of Public Health.

Located in New York City, Columbia provides exceptional internship and networking opportunities with hospitals, research institutes, regulatory agencies and biotech companies. The university’s interdisciplinary culture and Core Curriculum also allow students to develop broad intellectual skills alongside specialised statistical expertise, preparing them for careers that bridge quantitative methods and real‑world health problems.