Rochester Institute of Technology

What you'll study

The programme emphasises applied statistical methodology and statistical computing as they are used in biomedical and public-health research. Core topics typically include probability and statistical inference, linear models and regression, categorical data analysis, and computational statistics. Biostatistics-specific subjects commonly covered are survival analysis, longitudinal and repeated measures analysis, design and analysis of clinical trials, and applied methods for epidemiology.

Students also study modern computational tools and programming for data analysis, such as statistical programming in R and SAS, resampling and simulation methods, and Bayesian analysis. The curriculum is usually delivered through a combination of lectures, hands-on lab sessions and project-based modules.

The degree structure generally requires completion of coursework plus an applied capstone project or research thesis that demonstrates the ability to apply statistical methods to real biomedical problems. There are often options for internships or practicum placements with clinical research groups, healthcare providers or industry partners to gain practical experience.

Entry requirements

Applicants are expected to hold a bachelor’s degree in statistics, mathematics, data science, computer science, the biological sciences with substantial quantitative coursework, or a closely related field. Typical prerequisites include one year of calculus, introductory linear algebra, probability and mathematical statistics, and an introductory statistics course that includes regression.

Admission usually requires a completed graduate application, official transcripts, a personal statement outlining research or career goals, and academic references. Evidence of programming experience (for example in R, Python or SAS) and coursework in applied statistics will strengthen an application. Prospective students should check the university’s current guidance on standardised tests and language requirements, as these may vary.

Career prospects

Graduates with a master’s in statistics with a biostatistics emphasis move into roles that apply quantitative methods to biomedical and health data. Common job titles include biostatistician, clinical data analyst, statistical programmer, data scientist in healthcare, clinical trial statistician, and outcomes researcher.

Alumni find opportunities across pharmaceutical and biotechnology companies, clinical research organisations (CROs), hospitals and public-health agencies, academic research centres, and medical-device firms. The programme’s applied focus and practicum or capstone experience also prepare graduates for further study at the doctoral level or for positions that require close collaboration with clinicians and researchers.

Why study at Rochester Institute of Technology

RIT is known for its applied, career-oriented approach and strong emphasis on experiential learning. The university’s established cooperative education and internship networks in the Rochester region and beyond provide routes to industry and clinical research experience. Students benefit from accessible faculty who are active in applied statistical research and collaborations across health sciences, engineering and industry.

Facilities and resources supporting the programme include computing labs, access to statistical software, and opportunities to work on interdisciplinary projects with medical and public-health researchers. The programme’s practical orientation, industry links and focus on statistical computing make it a good choice for students aiming to develop the technical and collaborative skills demanded by employers in biostatistics and health-related data analysis.