Rice University

What you'll study

This programme combines core statistical theory and applied biostatistical methods. Core modules typically cover probability theory, mathematical statistics, linear models and regression, and computational statistics. Biostatistics-focused courses commonly include survival analysis, longitudinal data analysis, clinical trials design and analysis, categorical data methods, and causal inference.

Students also study modern data-intensive techniques such as Bayesian statistics, high-dimensional data analysis, bioinformatics genomics methods, and machine learning applied to biomedical problems. Practical training is emphasised through programming and software coursework (R, Python and relevant packages), a practicum or consulting component with real-world biomedical datasets, and a capstone project or optional research thesis supervised by faculty.

• Probability and Mathematical Statistics
• Linear Models and Regression
• Survival and Time-to-Event Methods
• Longitudinal and Repeated Measures Analysis
• Design and Analysis of Clinical Trials
• Bayesian Methods and Computational Inference
• Bioinformatics and High-Dimensional Data Analysis
• Statistical Consulting Practicum or Capstone Project

Entry requirements

Applicants should hold a bachelor’s degree (or equivalent) with substantial quantitative content—typically in statistics, mathematics, biostatistics, computer science, engineering or a related science. Strong preparation in calculus, linear algebra, introductory probability and mathematical statistics is expected. Prior exposure to programming (R, Python or similar) and to applied data analysis is highly recommended.

Typical application materials include an academic transcript, a personal statement outlining research and career goals in biostatistics, two or three letters of recommendation, and a CV or résumé. Some applicants may be asked to demonstrate quantitative readiness through coursework or to complete bridging courses if gaps are identified. English language proficiency evidence is required for applicants whose prior education was not in English.

Career prospects

Graduates of the programme enter a wide range of roles in the biomedical and health-data sectors. Common career paths include biostatistician in pharmaceutical and biotechnology companies, statisticians at contract research organisations (CROs), data scientist or analyst in health-tech firms, statisticians for governmental public-health agencies, and research statisticians in academic medical centres.

Specific responsibilities typically involve designing clinical trials, conducting statistical analyses for regulatory submissions, analysing observational and longitudinal health data, developing statistical methods for genomics and personalised medicine, and collaborating on interdisciplinary research teams. The programme also provides a foundation for those who wish to pursue doctoral study in biostatistics or statistics.

Why study at Rice University

Rice offers a close-knit academic environment with faculty who are active in both methodological research and applied biomedical collaborations. Students benefit from interdisciplinary connections across departments and from proximity to a major medical and biotechnology ecosystem, providing opportunities for practicum placements, internships and collaborative projects.

The university emphasises hands-on training in statistical computing and real-data problems, with accessible faculty mentorship and opportunities to participate in research groups and consulting services. Career support services and networking links within the Houston health-science community help students transition into roles in industry, government and academia following graduation.