University of Chicago

What you'll study

The programme combines core graduate-level statistics with specialised biostatistical coursework and applied projects. Core topics typically include probability theory, statistical inference, linear models and regression, and computational statistics (including MCMC and resampling methods). Biostatistics-specific modules cover survival analysis, longitudinal and repeated-measures data, causal inference for observational studies, design and analysis of clinical trials, and statistical methods for high-dimensional biological data such as genomics and RNA-seq.

Students also take practical courses in statistical computing and data science (R, Python, reproducible workflows), and have opportunities to study Bayesian methods, machine learning as applied to biomedical problems, and statistical genetics or bioinformatics depending on electives. The programme emphasises hands-on analysis through consulting, lab rotations or a capstone project/masters thesis that pairs students with faculty or clinical investigators, giving direct exposure to real biomedical datasets and collaborative research methods.

Entry requirements

Applicants are normally expected to hold a strong undergraduate degree in mathematics, statistics, computer science, engineering, economics, or a related quantitative discipline. Successful candidates typically have completed coursework in multivariable calculus, linear algebra, introductory probability and mathematical statistics; prior exposure to programming (e.g. R or Python) and an introductory course in statistical modelling is strongly recommended.

Typical application materials include official transcripts, a personal statement describing quantitative preparation and research or applied experience, a curriculum vitae, and letters of recommendation. International applicants must demonstrate English proficiency. The programme assesses candidates holistically; some applicants with non-traditional backgrounds but strong quantitative experience or relevant research may be considered.

Career prospects

Graduates go on to roles where statistical expertise drives biomedical decision-making. Common career paths include biostatistician or statistician roles in pharmaceutical and biotechnology companies, clinical research organisations (CROs), and hospitals; positions in academic research groups or public-health agencies analysing epidemiological and clinical data; and data scientist roles in health-tech and digital health startups. Some graduates continue to doctoral study in statistics, biostatistics or computational biology if they are aiming for research-focused careers.

Why study at University of Chicago

The University of Chicago offers rigorous theoretical training together with strong opportunities for interdisciplinary collaboration across medicine, genetics, computational biology and public health. Students benefit from close interaction with faculty who are active in methodological research and applied biomedical projects, and from access to clinical and laboratory collaborators at UChicago Medicine and affiliated institutes. The department emphasises a balance of theory, computation and real-data experience, preparing graduates to address complex problems in modern biomedical research and healthcare.