University of North Dakota

What you'll study

The programme combines rigorous theoretical training in probability and statistical inference with specialised coursework and applied projects in biostatistics. Early-stage core topics typically include advanced probability, asymptotic theory, linear models, and statistical computing. Biostatistics-focused modules commonly cover survival analysis, longitudinal and clustered data, generalized linear and mixed models, clinical trial design and analysis, Bayesian methods, causal inference and high‑dimensional data analysis (including genomics and bioinformatics applications).

Students usually undertake a mix of coursework, formal qualifying/comprehensive examinations, and original research leading to a dissertation. Practical training in statistical software (R, SAS, Python) and reproducible research practices is emphasised, as is experience in statistical consulting and collaboration with clinicians and biomedical researchers. Seminars and journal clubs expose students to current methodological advances and applied problems.

Entry requirements

Applicants are expected to have a strong quantitative background, normally demonstrated by a master’s degree in statistics, biostatistics, mathematics or a closely related field. Acceptable preparation includes graduate coursework in probability, mathematical statistics, linear models, and experience with statistical computing. Candidates with an outstanding bachelor’s degree and substantial research or applied experience may also be considered.

Typical application materials include official transcripts, a statement of purpose outlining research interests, letters of recommendation (usually two or three), and a résumé or curriculum vitae. Depending on the applicant’s background, a research writing sample or evidence of prior research may strengthen the application. International applicants must meet the university’s English language proficiency requirements.

Career prospects

Graduates are prepared for careers in academic research and teaching, leadership roles in biostatistics groups within pharmaceutical and biotechnology companies, positions at contract research organisations and clinical research units, and roles in government and public‑health agencies (for example, national health institutes and regulatory bodies). The programme also provides excellent preparation for data‑intensive roles such as bioinformatics, epidemiological modelling, and senior data science positions where deep statistical expertise and subject‑matter collaboration skills are required.

Why study at University of North Dakota

The University of North Dakota offers a close‑knit research environment with opportunities for cross‑disciplinary collaboration, particularly with the School of Medicine and Health Sciences and public health researchers. Students benefit from small cohort sizes and accessible faculty mentors, enabling tailored supervision on methodological and applied projects. Research links with clinical and population health units provide chances to work on real biomedical data and translational studies.

Doctoral students typically have access to professional development resources, computing facilities and opportunities for teaching or research assistantships that support training in communication, grant writing and collaborative consulting. The programme emphasises both methodological innovation and practical application, preparing graduates to contribute to advances in health research and statistics broadly.