University of Colorado Boulder

What you'll study

The undergraduate programme builds a core of statistical theory and methods alongside mathematics and computing skills, then offers biostatistics‑oriented electives and applied experiences. Core topics include introductory and intermediate statistics, probability theory, mathematical statistics, linear models and regression, and statistical inference.

• Mathematical foundations: calculus sequence, linear algebra and probability.
• Core statistics: inferential statistics, theory of estimation and hypothesis testing, regression and analysis of variance, generalized linear models.
• Computing and data skills: statistical computing with R and Python, data management, reproducible research, and data visualisation.
• Biostatistics electives: introduction to epidemiology, statistical methods for clinical trials, survival analysis, categorical and longitudinal data analysis, statistical genetics and bioinformatics.
• Applied and experiential components: practicum or internship placements with research groups, the Colorado School of Public Health or local biotech firms, plus options for an honours thesis or capstone project applying methods to real biological or clinical datasets.

Students typically combine statistics coursework with electives in biology, public health, computer science or chemistry to tailor the degree to specific biological applications.

Entry requirements

Admission is to the university and then to the statistics major following college policies. Typical preparation includes strong performance in high‑school mathematics, especially calculus, and coursework in science. Successful applicants generally demonstrate:

• Solid grades in mathematics (calculus) and quantitative subjects;
• Experience or proficiency in programming or data analysis is advantageous (for example Python, R or similar tools);
• Recommended background in biology or chemistry for students aiming at the biostatistics track;
• Fulfilment of the University of Colorado Boulder undergraduate admission standards; departmental progression may require completion of specific lower‑division statistics and mathematics courses with minimum grades.

Standardised test requirements may vary; consult the university’s admissions pages for current policies and any unit‑specific progression criteria.

Career prospects

Graduates with a biostatistics emphasis are well placed for careers that bridge quantitative analysis and the life sciences. Common roles include:

• Biostatistician or statistical analyst in pharmaceutical and biotechnology companies;
• Data scientist or analyst in clinical research organisations and medical device firms;
• Analyst or methodologist in public health agencies, hospitals and health systems;
• Research support roles in academic labs, medical schools and government research institutes;
• Further study at graduate level (master’s or PhD) in biostatistics, statistics, epidemiology, bioinformatics or related fields for research and academic careers.

Undergraduate training in programming, applied modelling and communication of statistical results makes graduates attractive to employers that require reproducible workflows and collaboration with multidisciplinary teams.

Why study at University of Colorado Boulder

CU Boulder offers an environment that supports applied statistical training with ready access to health, biological and technology research across the campus. The university collaborates closely with the Colorado School of Public Health and research institutes that provide internship and undergraduate research opportunities relevant to biostatistics.

• Opportunities for hands‑on projects and practicum placements with local biotech firms, hospitals and public health bodies.
• Access to research centres and interdisciplinary centres focused on molecular biology, bioinformatics and translational health research.
• Emphasis on statistical computing and reproducible science, with undergraduate support for learning R, Python and modern data workflows.
• Career services and departmental advising that help link students to internships, summer research and graduate study pathways.

For students seeking a quantitative degree that directly prepares them to work with biological and clinical data, the programme combines rigorous theory, practical computing skills and applied experiences in a research‑active university setting.