University of Kansas

What you'll study

The programme combines core statistical theory and applied biostatistical methods with computing skills used in contemporary biomedical research. Core topics typically include probability, statistical inference, linear and generalized linear models, and computational statistics. Biostatistics‑focused modules commonly cover survival analysis, longitudinal and repeated measures, clinical trials design and analysis, categorical data analysis and methods for high‑dimensional/omics data.

• Statistical theory and inference
• Regression and generalized linear models
• Survival analysis and time‑to‑event methods
• Longitudinal data and mixed‑effects models
• Design and analysis of clinical trials
• Computational methods and programming (R, SAS, or equivalent)
• Advanced topics: high‑dimensional data, Bayesian methods, causal inference
• Applied practicum or research project / thesis option

Students typically choose between a coursework‑based pathway with an applied practicum and a research pathway that includes a master’s project or thesis. Practical training emphasises real data analysis, reproducible research practices and collaboration with researchers in medicine and public health.

Entry requirements

Applicants are expected to hold a bachelor’s degree in statistics, mathematics, biostatistics, computer science, engineering or a closely related quantitative discipline. Strong preparation in calculus, linear algebra, introductory probability and mathematical statistics is important.

• Official transcripts demonstrating a solid quantitative background
• Statement of purpose outlining research or career objectives
• Curriculum vitae or résumé
• Letters of recommendation (usually two or three) from academic or professional referees
• International applicants must provide evidence of English language proficiency where required

Some applicants may be asked to provide GRE scores depending on department policy; relevant professional experience in data analysis or prior research can strengthen an application. Preparatory or bridge courses may be recommended for applicants who lack specific mathematical or programming coursework.

Career prospects

Graduates of the programme are prepared for roles as biostatisticians, data analysts and quantitative scientists in a range of settings. Typical employers include:

• Academic medical centres and university research groups
• Pharmaceutical and biotechnology companies
• Clinical research organisations (CROs)
• Public‑health agencies and government research institutions
• Healthcare technology and medical devices firms
• Health‑focused non‑profits and policy organisations

Career paths range from applied analysis and trial design to positions in regulatory science, epidemiology collaborations, data science roles that focus on biomedical problems, or further graduate study (PhD) in biostatistics, statistics or related fields.

Why study at University of Kansas

The University of Kansas offers a collaborative environment where statistics and biostatistics students can work closely with clinicians, epidemiologists and basic scientists. Students benefit from access to clinical and translational research activities at KU Medical Center and affiliated research centres, which provide opportunities for practicum placements and applied projects.

Faculty conduct research across a broad set of biomedical areas, so students can gain experience in clinical trials, cancer biostatistics, genomics and population health. The department emphasises strong computing training and reproducible research practices, and students have access to institutional computing resources and statistical software. Small class sizes help ensure personalised mentoring and support for career development and graduate study preparation.