University of Arizona

What you'll study

This interdisciplinary degree builds a foundation in mathematics, computation and biological science. Early coursework typically covers calculus, linear algebra, probability and statistics, introductory programming (commonly Python and/or R), molecular and cell biology, and general chemistry. Progressing modules introduce numerical methods, differential equations, data structures and algorithms, machine learning, and statistical genomics.

Specialist topics emphasise the application of quantitative methods to biological problems and may include:

• Computational genomics and sequence analysis
• Structural bioinformatics and protein modelling
• Biological data visualisation and large‑scale data management
• Stochastic and deterministic models of biochemical and ecological systems
• Population genetics and evolutionary modelling
• High‑throughput data analysis, reproducible computing and workflow development

The programme usually includes laboratory‑style courses and project work, with opportunities for a capstone project or honours thesis involving original computational research. Students are encouraged to engage in undergraduate research with faculty and to take elective modules in areas such as bioengineering, statistics, or computer science to match career goals.

Entry requirements

Applicants are expected to demonstrate strong preparation in mathematics and science. Typical requirements include successful completion of high‑school level calculus and biology, and usually chemistry; prior experience in programming or computer science is highly desirable. Admissions decisions consider secondary school transcripts, personal statements and academic references.

For transfer or internal applicants, prior university coursework in calculus, introductory biology and programming will be taken into account. International applicants should meet the University of Arizona's standard English proficiency requirements and provide equivalent academic documentation from their schooling system.

Career prospects

Graduates are prepared for a wide range of roles that bridge biology and quantitative computation. Common career paths include bioinformatics analyst, computational biologist, data scientist in biotech or healthcare, biostatistician, and roles in pharmaceutical discovery such as computational drug design. Many alumni progress to graduate study (MSc or PhD) in computational biology, bioinformatics, biostatistics or related fields, while others move into interdisciplinary roles in public health, agricultural genomics, or start‑ups focused on computational life‑science solutions.

Why study at University of Arizona

The University of Arizona offers strong interdisciplinary support for computational biology through collaborative research institutes and on‑campus core facilities. Students benefit from access to faculty conducting active research in genomics, systems biology and applied mathematics, and can pursue undergraduate research placements or internships with research groups and local industry partners. The programme emphasises hands‑on data analysis and project experience, preparing graduates to work with large biological datasets and modern computational tools.