University of Arizona

What you'll study

The programme builds a strong foundation in probability, statistical inference and mathematical modelling, and applies those tools to problems in biology, medicine and public health. Core coursework typically includes calculus and linear algebra, introductory and intermediate statistics, probability theory, regression and generalized linear models, experimental design and statistical computing (including R and Python).

• Foundations: calculus sequence, linear algebra and an introduction to discrete mathematics where relevant.
• Core statistics: introductory and intermediate statistical inference, probability, regression analysis and multivariate methods.
• Biostatistics-focused modules: statistical methods for biomedical research, survival analysis, longitudinal data analysis, clinical trials methodology and bioinformatics/statistical genomics electives.
• Computing and data science: statistical computing, data management, reproducible research and courses using R, Python and data visualisation tools.
• Practical experience: a required capstone or senior project, applied consulting practicum or internships that place students in research labs, hospitals, public health departments or industry settings.

Students may also take allied life-science courses such as introductory biology, molecular biology, epidemiology and courses offered through the university's biomedical research institutes to gain domain knowledge necessary for applied biostatistical work.

Entry requirements

Admission is through the University of Arizona undergraduate admissions process; competitive applicants will demonstrate strong achievement in mathematics and quantitative coursework. Typical expectations include a completed secondary-school qualification with high marks in mathematics (for example A‑level, IB Higher Level maths, or equivalent), and coursework in a science is advantageous.

• Academic background: strong performance in algebra, precalculus/calculus and statistics where available; science courses such as biology are recommended.
• Standardised tests and evidence: the university considers a range of academic indicators. Applicants should consult the admissions office for current testing policies and optional test submission guidance.
• Transfer and internal progression: students transferring from community colleges or other universities will typically need to have completed prerequisite mathematics courses with competitive grades; current University of Arizona students seeking the major must meet department prerequisites and GPA requirements.
• International students: completion of an equivalent secondary qualification is required; proof of English language proficiency is required where applicable.

Career prospects

Graduates with a bachelor's in statistics focusing on biostatistics are prepared for roles that require the design and analysis of studies in health, biology and environmental sciences. Entry-level positions include statistical analyst, biostatistical assistant, data analyst in healthcare or pharmaceutical companies, clinical data coordinator and research support roles in academic or government labs.

• Common employers include hospitals, public-health agencies, pharmaceutical and biotech firms, clinical research organisations and academic research centres.
• Graduates frequently progress to graduate study (Master's or PhD) in biostatistics, statistics, epidemiology, public health or data science to qualify for senior biostatistician or research scientist roles.
• Skills learned also transfer to broader data science and analytics careers in industry sectors that value rigorous quantitative and computational expertise.

Why study at University of Arizona

The University of Arizona offers this programme within a research-active environment that emphasises interdisciplinary collaboration between statistics, biomedical sciences and public health. Students benefit from proximity to world-class research units and health-science centres, opportunities for undergraduate research, and collaborations with institutes focused on biomedical and environmental health.

• Research and internship opportunities: connections with the university's biomedical research institutes and local healthcare community provide placements and project opportunities for applied experience.
• Computing and resources: strong computational training, access to statistical software and high-performance computing resources to support data-intensive projects.
• Support for progression: advising and mentoring for students planning careers in industry or for further study in graduate programmes in biostatistics, public health or related fields.