Saint Michael's College

What you'll study

The programme builds a foundation in calculus, linear algebra and probability before moving into core statistical theory and applied methods. You will study courses such as:

• Calculus and Linear Algebra: multivariable calculus and matrix methods that underpin statistical modelling.
• Probability and Mathematical Statistics: probability theory, estimation, hypothesis testing and asymptotics.
• Regression and Generalised Linear Models: linear regression, logistic regression, Poisson models and model selection.
• Design of Experiments and ANOVA: principles of experimental design, blocking, randomisation and analysis of variance.
• Biostatistics and Applied Methods: survival analysis, longitudinal data methods, epidemiologic study designs and analysis of clinical trial data.
• Statistical Computing and Data Science: intensive training in R (and other tools), data cleaning, simulation, reproducible research and introductory machine learning techniques.
• Advanced Topics and Electives: Bayesian statistics, multivariate analysis, time series and specialised courses in genomics or environmental statistics when available.
• Capstone and Research Experience: a senior project or practicum that emphasises applied problems in biology, public health or clinical research, often involving real datasets and a written report plus presentation.

Coursework emphasises both theoretical understanding and practical experience with real biomedical datasets; lab‑style classes and consulting projects are commonly used to develop communication of statistical results to non‑statistical audiences.

Entry requirements

Applicants should hold a high school diploma or equivalent with a strong academic record. Typical requirements include:

• A background in mathematics with successful completion of algebra and geometry; completion of pre‑calculus or calculus is strongly recommended.
• Evidence of quantitative aptitude, which may include high grades in mathematics courses, scores from standardised tests where provided, or placement test results.
• General admission materials such as transcripts, a personal statement explaining interest in statistics/biostatistics, and letters of recommendation.
• For applicants whose first language is not English, proof of English proficiency through accepted qualifications or testing is required.

Saint Michael’s College evaluates applicants holistically, considering academic preparation, demonstrated interest in the field, and potential for success within a small liberal‑arts setting. Prospective students are encouraged to contact the department to discuss preparatory coursework if their background in calculus or programming is limited.

Career prospects

Graduates with a bachelor’s in statistics and a focus on biostatistics are prepared for roles that combine quantitative analysis with biological and health sciences. Common career paths include:

• Biostatistician or statistical analyst in pharmaceutical companies, biotechnology firms and clinical research organisations.
• Data analyst or research analyst in public health agencies, hospitals and non‑profit health organisations.
• Clinical trial coordinator or statistical programmer supporting study design, data management and regulatory reporting.
• Analyst roles in government health departments, environmental agencies and policy institutes.
• Further study: many graduates pursue graduate degrees (MSc or PhD) in biostatistics, statistics, epidemiology or related fields to advance into research or specialised applied roles.

The programme emphasises marketable skills — statistical modelling, coding in R, data visualisation and scientific communication — that employers value across healthcare and research sectors.

Why study at Saint Michael's College

Saint Michael’s College offers a small, student‑focused liberal‑arts environment where undergraduates receive close mentoring from faculty. The statistics/bioinformatics curriculum is designed to provide hands‑on experience through lab courses, capstone projects and opportunities for independent research under faculty supervision.

• Small class sizes and accessible faculty: personalised attention and strong advising help students develop both technical skills and communication abilities.
• Experiential learning: emphasis on applied projects, internships and practicum experiences with regional healthcare providers and research groups in New England.
• Computing and support resources: access to statistical software, computing labs and guidance on reproducible research and data ethics.
• Liberal‑arts context: interdisciplinary offerings allow students to combine statistics with biology, public health, environmental science or other fields to tailor their education towards specific biostatistical applications.

Overall, the programme is well suited to students seeking a solid quantitative foundation plus practical experience to enter biomedical and public‑health data roles or to continue into graduate study.