University of North Dakota

What you'll study

The programme builds core competency in probability, mathematical statistics and applied data analysis, while emphasising computational skills and practical applications in the life sciences. First-year and early undergraduate modules typically cover calculus, linear algebra and introductory statistics to establish the mathematical background.

• Probability and Mathematical Statistics – probability theory, distributions, sampling theory and point/interval estimation.
• Applied Statistical Methods – regression and ANOVA, categorical data analysis and generalized linear models.
• Computational Statistics – statistical computing with R and/or SAS, simulation, resampling methods and data management.
• Design and Analysis of Experiments – experimental design, blocking, factorial experiments and analysis strategies.
• Biostatistical Topics – survival analysis, longitudinal data analysis, clinical trials methodology and biometrics (usually available as upper‑level electives).
• Advanced and Elective Options – Bayesian methods, multivariate analysis, time series and specialized courses aligned with biology, public health or bioinformatics.
• Capstone / Practicum – a senior project, internship or practicum that applies statistical methods to a real dataset; opportunities to collaborate with the UND School of Medicine & Health Sciences or local research partners.

The curriculum also integrates coursework in computing and data handling, and encourages undergraduate research projects supervised by faculty in the Department of Mathematics & Statistics or cross‑departmental collaborations.

Entry requirements

Applicants should have a high school diploma or equivalent with strong achievement in mathematics. Typical preparation includes algebra, geometry and a year of calculus where available. Admissions will consider overall academic record, recommendations and any relevant coursework or experience in mathematics and science.

• Strong foundations in algebra and introductory calculus are expected; further preparation in statistics is helpful but not always required.
• Successful applicants normally demonstrate quantitative aptitude through grades in relevant subjects; placement testing or first‑year mathematics courses may be used to determine appropriate course level.
• For non‑native English speakers, evidence of English proficiency is required in line with university policy.

Career prospects

Graduates with a bachelor’s in statistics and biostatistics interests have a range of career options in health, life sciences and beyond. The degree provides the quantitative and computing skills employers seek for data‑driven roles.

• Biostatistician / Statistical Analyst – roles in hospitals, public health agencies, research institutes and pharmaceutical companies supporting study design and data analysis.
• Clinical Trials Analyst – positions analyzing clinical study data, working with regulatory submissions and ensuring data integrity.
• Data Scientist / Data Analyst – industry roles in healthcare analytics, insurance, biotech and startups that require statistical modelling and programming skills.
• Research and Policy – supporting epidemiological studies, population health research and health outcomes analysis for government or NGOs.
• Further study – many graduates progress to master’s or doctoral programmes in biostatistics, statistics, epidemiology, public health or applied mathematics for advanced research or specialist roles.

Why study at University of North Dakota

The University of North Dakota offers a statistics programme anchored in a department with active faculty research and cross‑disciplinary links to the School of Medicine & Health Sciences. Students benefit from hands‑on experience with real health datasets, opportunities for undergraduate research and close faculty mentorship.

• Collaborative opportunities with UND’s health sciences and rural health centres provide practical experience relevant to biostatistics.
• Small to medium class sizes allow personalised instruction in advanced statistical methods and computing.
• Access to computing resources and standard statistical software (such as R and SAS) prepares students for applied roles.
• Clear pathways to graduate study and professional placement, supported by faculty who supervise senior projects and practica in applied health research.