Loyola University Chicago

What you'll study

The programme combines core statistical theory with applied biostatistical methods and computing. Core topics typically include probability and statistical inference, linear and generalized linear models, and multivariate methods. Applied biostatistics modules commonly cover clinical trials design and analysis, survival analysis, longitudinal data analysis, categorical data methods, and causal inference.

Students gain hands‑on experience with statistical software (for example R and SAS) and methods for reproducible research. Coursework is often complemented by electives in topics such as Bayesian statistics, high‑dimensional data analysis, epidemiologic methods, and advanced computational statistics. The programme usually culminates in a capstone project, practicum with a clinical or public‑health partner, or a research thesis that applies statistical methods to real biomedical data.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in statistics, mathematics, biostatistics, epidemiology, computer science, or a closely related quantitative discipline. Typical academic preparation includes courses in calculus (through multivariable), linear algebra, introductory probability and statistics, and some programming experience.

Admissions materials generally include official transcripts, a personal statement describing research and career goals, a current résumé or CV, and one or more letters of recommendation. Some applicants without a formal statistics degree may be admitted conditional on completing prerequisite coursework. Check the university’s admissions pages for specific document requirements and any standardised-test policy.

Career prospects

Graduates work as biostatisticians, statistical programmers, data analysts, and quantitative researchers across healthcare, industry and government. Typical employers include pharmaceutical and biotechnology companies, contract research organisations (CROs), academic medical centres, public‑health agencies, hospitals and health systems, and health‑data startups.

Common roles involve the design and analysis of clinical trials, observational-study analysis, development and implementation of statistical methods for genomics and high‑throughput data, real‑world evidence studies, and collaborative research with clinicians. The programme also prepares students for further study at the doctoral level or for roles that bridge data science and biomedical research.

Why study at Loyola University Chicago

Loyola University Chicago offers a close connection between statistical training and clinical and public‑health practice through interdisciplinary collaboration with its health science and medical schools. Students benefit from access to clinical sites and research projects in the Chicago healthcare community, enabling applied practicum opportunities and real‑world datasets.

The programme emphasises ethical practice and service to the community, reflecting Loyola’s Jesuit mission, and typically features small cohort sizes that support close faculty mentoring. Practical training in statistical computing, collaborative research experience, and targeted electives help graduates move directly into biostatistics roles or continue into doctoral study.