University of Vermont

What you'll study

The degree combines core statistical theory with applied methods used in biological and health sciences. Early modules build a foundation in calculus, linear algebra, probability and introductory statistics, followed by intermediate and advanced courses in mathematical statistics, regression and multivariate analysis.

• Core mathematics and probability: single‑variable and multivariable calculus, linear algebra, and probability theory that underpin statistical methods.
• Foundations of statistics: mathematical statistics, estimation and hypothesis testing, likelihood methods.
• Applied and computational statistics: regression, generalized linear models, survival analysis, experimental design and categorical data analysis, with a strong emphasis on computation using R and/or Python.
• Biostatistics and interdisciplinary subjects: courses in epidemiology, clinical trials methodology, longitudinal data analysis, and bioinformatics or genomics data analysis depending on elective choices.
• Data science and practice: data management, statistical computing, reproducible research, and project‑based courses or a capstone that involve analysing real biological or health datasets.
• Research and electives: opportunities for undergraduate research with faculty in the Department of Mathematics & Statistics or collaborations with health sciences departments; elective options allow tailoring toward public health, ecology or computational biology.

Entry requirements

Applicants are expected to have a strong background in high‑level secondary mathematics. Typical preparation includes calculus and additional coursework that demonstrates quantitative aptitude. Schools commonly ask for:

• High school diploma or equivalent with strong grades in mathematics (calculus strongly recommended).
• Successful performance in algebra, precalculus and preferably calculus; familiarity with statistics is beneficial but not always required.
• For international applicants, equivalent qualifications such as A‑levels, IB diploma or country‑specific credentials demonstrating comparable achievement in mathematics.
• Recommended preparation includes introductory programming or computer science and coursework in biology if you plan to concentrate in biostatistics.

Admissions also consider overall academic record, essays, and other application materials. Specific grade thresholds and credential policies are set by the university and may vary.

Career prospects

Graduates are prepared for a variety of roles that apply statistical methods to biological and health contexts. Common career paths include:

• Biostatistician or statistical analyst in pharmaceutical companies, biotechnology firms and contract research organisations.
• Data analyst or epidemiological analyst in public health agencies, hospitals or health systems.
• Clinical trials analyst and data manager supporting clinical research and regulatory submissions.
• Research assistant in university or government laboratories working on experimental design and data analysis.
• Further study at graduate level in biostatistics, statistics, public health or data science leading to specialised careers in academia, industry or policy.

The programme’s combination of theory, computing and applied projects equips graduates with the technical skills and communication ability employers look for in healthcare and life‑science settings.

Why study at University of Vermont

The University of Vermont offers a close‑knit academic environment where undergraduates can work directly with faculty who are active in statistical research and collaborate with health and life‑science departments. The university’s connections with local hospitals and research centres provide practical project and internship opportunities relevant to biostatistics.

Students benefit from hands‑on computing facilities, a curriculum that emphasises reproducible data analysis and opportunities for interdisciplinary study with public health, biology and engineering. Small class sizes and accessible faculty mentoring make it easier to undertake undergraduate research or capstone projects that build a portfolio of applied analyses attractive to employers and graduate programmes.