University of Iowa

What you'll study

The programme builds a foundation in probability and statistical inference while emphasising methods used in biomedical and public-health research. Core topics typically include linear models, generalized linear models, categorical data analysis, survival analysis, longitudinal data analysis, and design and analysis of clinical trials. Students also study statistical computing (often with R and SAS), Bayesian methods, and modern machine-learning approaches as applied to biological data.

• Core coursework: probability, mathematical statistics, regression and linear models, and statistical computing.
• Applied methods: survival and longitudinal analysis, generalized linear models, categorical data, and clinical trials design and analysis.
• Computing and data management: programming in R/SAS, reproducible research, data wrangling, and simulation studies.
• Electives and special topics: Bayesian methods, causal inference, bioinformatics and high-dimensional data methods (availability varies by semester).
• Capstone experience: a supervised practicum or thesis/project option that places students in collaborative research settings with faculty, the university hospitals, or external partners.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree with substantial quantitative training. Typical preparation includes coursework in calculus, linear algebra, probability, and an introductory statistics sequence; prior exposure to mathematical statistics and programming is highly desirable.

• Academic record: a bachelor’s degree in statistics, mathematics, biostatistics, computer science, engineering, or a closely related field, with a solid transcript in quantitative subjects.
• Prerequisites: calculus and linear algebra; an introductory course in probability and statistics; prior experience with statistical computing or programming is recommended.
• Supporting materials: transcripts, statement of purpose describing research or career goals, and letters of recommendation. The programme may provide guidance on whether GRE scores are required or considered; English language proficiency test scores are requested from applicants whose native language is not English.
• International applicants: evidence of quantitative preparation equivalent to the US bachelor’s level and demonstration of English proficiency where applicable.

Career prospects

Graduates move into roles where quantitative and collaborative skills are essential. Common career destinations include:

• Biostatistician or statistical programmer in pharmaceutical or biotechnology companies.
• Analyst or researcher in academic medical centres, public-health agencies, hospital research units, and contract research organisations.
• Data scientist or quantitative analyst positions in health informatics, medical devices, and health technology startups.
• Further study: many students progress to doctoral programmes in biostatistics, statistics or related fields if they are interested in research or academic careers.

Why study at University of Iowa

The University of Iowa offers close interdisciplinary collaboration between the Department of Biostatistics and the Carver College of Medicine, providing access to clinical investigators, patient-centred research and hospital data for applied training. Faculty are engaged in a broad range of areas — clinical trials, epidemiology, genomics, and health-care analytics — giving students opportunities to join active research projects.

Students benefit from a balance of rigorous theoretical training and practical experience through practicum placements and project-based coursework. The department emphasises statistical computing and reproducible research, and students have access to university computing resources and statistical consulting opportunities. Career services and connections with regional health-care and biopharma employers support transitions into professional roles or further doctoral study.