Rochester Institute of Technology

What you'll study

The curriculum balances foundations in probability and mathematical statistics with applied and computational courses tailored to biological and health applications. You study core topics such as probability theory, mathematical statistics, and statistical inference, alongside applied modules in regression, experimental design and multivariate analysis. Emphasis is placed on statistical computing and data science skills using tools and languages commonly used in the life sciences.

• Core statistics and mathematics: calculus, linear algebra, probability, mathematical statistics, statistical inference.
• Applied and biostatistics: regression analysis, generalized linear models, design of experiments, survival analysis, longitudinal data analysis, clinical trials methodology.
• Computational methods: statistical programming in R and Python, data management, reproducible research, introduction to SAS and SQL for health data.
• Interdisciplinary options: bioinformatics, computational biology, epidemiology, public health statistics, laboratory or clinical practicum electives when available.
• Capstone and experiential learning: a senior capstone project or research placement emphasising real biological datasets, and opportunities for internships or cooperative education placements with industry or health research groups.

Entry requirements

Admission is based on a strong secondary‑school academic record with particular emphasis on mathematics. Applicants should have completed calculus or equivalent preparatory coursework; background in biology, chemistry or statistics is helpful for the biostatistics focus. Universities typically consider the overall transcript, personal statement, and recommendation letters; applicants who submit standardised test scores may strengthen their application, depending on institutional policy. International applicants must meet English language proficiency requirements and have equivalent qualifications to the US secondary diploma.

Career prospects

Graduates are prepared for quantitative roles that bridge statistics and the life sciences. Career paths include positions in pharmaceutical and biotechnology companies, healthcare institutions, clinical research organisations, public health agencies and research laboratories.

• Biostatistician or clinical statistician working on clinical trials and regulatory submissions.
• Statistical programmer or data analyst in pharma, biotech or medical device companies.
• Research analyst or quantitative specialist in public health, epidemiology or government health agencies.
• Data scientist or machine learning engineer applying statistical models to genomic, proteomic or other biomedical data.
• Progression to graduate study (MS or PhD) in biostatistics, statistics, epidemiology, or related fields for research and academic careers.

Why study at Rochester Institute of Technology

RIT emphasises experiential learning and industry engagement, which benefits statistics students through cooperative education placements, internships and project‑based courses that use real biomedical datasets. The university's strong computing infrastructure and cross‑disciplinary links with health‑related departments enable students to develop practical programming and data management skills alongside statistical theory. Career services and employer networks at RIT support transitions into roles in industry and research, and the programme's applied orientation prepares graduates for both immediate employment and further study in biostatistics and related areas.