University of North Dakota

What you'll study

The programme combines core statistical theory with applied biostatistical methods and computing. Core topics typically include probability and statistical inference, linear models, and applied regression. Biostatistics-focused modules cover survival analysis, longitudinal and repeated-measures methods, categorical data analysis, clinical trial design and analysis, and methods for observational studies.

Training in statistical computing and data management is emphasised: coursework and labs use R and statistical software for reproducible analysis, simulation and data visualisation. Students normally complete a capstone experience that may be a supervised practicum with a health‑science research group, a project-based course working on real biomedical datasets, or a research thesis for those pursuing deeper methodological work.

• Probability and Statistical Inference
• Linear Models and Regression
• Survival Analysis and Time-to-Event Methods
• Categorical Data and Generalised Linear Models
• Longitudinal Data Analysis
• Design and Analysis of Clinical Trials
• Statistical Computing, Data Management and Reproducible Research
• Applied Biostatistics Practicum or Thesis

Programme structure

The programme is offered as a coursework master’s with options for a thesis or project capstone. Students pursue a combination of required core courses, elective biostatistics modules and a culminating experience. Typical programmes require completion of a specified number of graduate credit hours, including a research or practicum component that places students in collaborative health‑science teams or supervised research.

Entry requirements

Applicants should hold a bachelor’s degree from an accredited institution. Preferred backgrounds include mathematics, statistics, physics, computer science, engineering, biology or other quantitative disciplines. Typical preparation includes calculus (single and multivariable), linear algebra, introductory probability and statistics, and some exposure to programming or data analysis (for example, experience with R, Python, SAS or similar).

• Official undergraduate transcripts showing a strong quantitative record.
• A personal statement describing academic and career goals, and interest in biostatistics.
• Curriculum vitae or résumé highlighting relevant technical skills and experience.
• Letters of recommendation from academic or professional referees.
• Proof of English proficiency for international applicants where required.

Some applicants without a full quantitative background may be admitted conditionally and asked to complete specified prerequisite courses. Consult the department for details on prerequisite coursework and any standardised testing policies.

Career prospects

Graduates are prepared for roles as biostatisticians and data analysts in a wide range of health‑related settings. Common employers and roles include:

• Biostatistician or data scientist in pharmaceutical and biotechnology companies.
• Statistical analyst in contract research organisations (CROs) supporting clinical trials.
• Analyst or researcher in academic medical centres, public‑health agencies and hospitals.
• Research statistician in government health departments, non‑profit health organisations and clinical research networks.
• Continuing to doctoral study (PhD) in statistics, biostatistics or related fields for those pursuing academic or advanced research careers.

Practical experience gained through practicum placements and collaborative projects is a strong asset when seeking employment in clinical research and public‑health teams.

Why study at University of North Dakota

The University of North Dakota offers a programme with strong connections to the health‑science and medical research community on campus, enabling applied collaborations and practicum placements with clinicians and public‑health researchers. Small cohort sizes allow personalised mentoring from faculty with expertise in statistical theory and applied biostatistics.

Students benefit from hands‑on training in contemporary statistical computing and access to interdisciplinary research opportunities with the School of Medicine & Health Sciences and related centres. The department emphasises close faculty supervision for capstone projects and thesis work, preparing graduates for professional roles in industry, government and academic research.

Prospective students should contact the Department of Mathematical Sciences or the programme coordinator for details on course options, research supervisors and how the biostatistics emphasis can be tailored to individual career goals.