Syracuse University

What you'll study

The programme combines core statistical theory with applied biostatistical methods and computing. Core topics typically include probability theory, statistical inference, linear models and regression, and statistical computing (primarily using R and other open‑source tools). Biostatistics‑focused modules commonly cover survival analysis, longitudinal data analysis, categorical data analysis, clinical trials design and analysis, and Bayesian methods for health data.

Students also study practical subjects such as data management, reproducible research, and applied statistical consulting. Many programmes include a capstone project, practicum or thesis option that lets students work on real datasets from public health, clinical trials or genomics, gaining experience in study design, analysis plans and regulatory reporting.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree with substantial quantitative content, such as mathematics, statistics, economics, engineering, computer science or a related field. Typical background preparation includes one year each of calculus and linear algebra, an introductory course in probability and statistics, and some programming experience (for example in R, Python or MATLAB).

Admissions usually consider academic transcripts, letters of recommendation, a statement of purpose outlining research and career objectives, and a CV. Some programmes may request or consider GRE scores if submitted, and international applicants will need to demonstrate English proficiency. Relevant professional experience in health, research or data analysis is advantageous for applicants without a formal statistics major.

Career prospects

Graduates with a master’s in biostatistics work across healthcare, academia, government and industry. Typical roles include biostatistician or statistician in pharmaceutical and biotechnology companies, statistical programmer or analyst at contract research organisations (CROs), data scientist or analyst in health technology firms, and public‑health statistician for local, national or international health agencies.

Other common paths are research support positions in academic medical centres, roles in clinical trial design and monitoring, and further study at the doctoral level for students pursuing academic or high‑level research careers. The combination of statistical theory, applied methods and computing skills emphasised by the programme prepares graduates for responsibilities in study design, data analysis, regulatory submissions and collaborative research teams.

Why study at Syracuse University

Syracuse University offers an environment that emphasises applied, interdisciplinary training and close faculty mentorship. Students benefit from hands‑on training in contemporary statistical computing and opportunities to collaborate with researchers across departments, developing practical skills valued by employers.

The university supports experiential learning through capstones, practicum placements and partnerships that help students build professional networks in health research and industry. Small cohorts and accessible faculty supervision allow for personalised research projects and career guidance, helping graduates move into roles in clinical research, public health, biopharmaceuticals and related fields.