Indiana University Bloomington

What you'll study

The programme combines core statistical theory with applied biostatistical methods and computational training. Core topics typically include probability theory, statistical inference, linear models and regression analysis, and statistical computing (with an emphasis on R and other scientific software). Biostatistics-specific coursework commonly addresses survival analysis, longitudinal data analysis, clinical trials methodology, categorical data analysis, and methods for high-dimensional and genomics data.

Students encounter both theoretical and applied components: mathematical foundations and proof-based work alongside projects, data analysis assignments and a practicum or thesis option. Training in modern topics such as Bayesian methods, causal inference, and reproducible research is often available through elective modules or seminars. Practical experience is supported by collaborations with public-health researchers and biomedical groups on campus.

• Core statistical theory (probability, inference, linear models)
• Applied biostatistics (survival analysis, longitudinal methods, clinical trials)
• Statistical computing and data science (R, reproducible workflows, simulation)
• Electives in Bayesian statistics, causal inference, high-dimensional data and genomics
• Research project, practicum or master's thesis option

Entry requirements

Applicants are expected to hold a bachelor's degree in statistics, mathematics, biostatistics, computer science or a closely related quantitative discipline. A strong foundation in calculus, linear algebra, introductory probability and mathematical statistics is normally required, together with experience in programming for data analysis (for example, R, Python or SAS).

Typical application materials include official transcripts, a statement of purpose explaining quantitative background and research or career goals, letters of recommendation, and a curriculum vitae. International applicants must demonstrate English proficiency by an accepted test unless exempt. Some applicants may be invited to interview; research or applied experience in statistics, biology or public health strengthens an application.

Career prospects

Graduates with a master's in biostatistics are prepared for roles as biostatisticians, clinical trial statisticians, data scientists in healthcare, epidemiology analysts, and statistical programmers. Common employers include hospitals and health systems, pharmaceutical and biotech companies, contract research organisations (CROs), government public-health agencies, and academic research groups. The programme also supplies a strong foundation for those who choose to continue to doctoral study.

Alumni typically work on the design and analysis of clinical trials, development of statistical methods for observational studies, analysis of large biomedical datasets (including genomic data), and collaboration on interdisciplinary research teams translating statistical results into policy or clinical practice.

Why study at Indiana University Bloomington

Indiana University Bloomington offers an environment with active statistical research and close interdisciplinary collaboration across public health, medicine and life sciences. Students benefit from access to experienced faculty working in methodological and applied biostatistics, as well as opportunities to join consulting services and research projects on campus. The department emphasises training in both rigorous theory and practical computing skills, preparing graduates for applied roles in research and industry.

The campus provides computing resources, regular seminars and workshops, and connections to regional healthcare and research organisations for practicum and internship experiences. A supportive graduate community, opportunities for teaching or research assistantships, and proximity to interdisciplinary centres make IU Bloomington a practical choice for students seeking comprehensive training in biostatistics.