University of San Diego

What you'll study

This programme blends foundational coursework in mathematics, statistics and computer science with core biological subjects to give students the quantitative tools needed to analyse genomic, proteomic and systems-level biological data. Teaching methods include lectures, lab practicals, programming projects and a senior capstone or research thesis.

• Foundations in mathematics and computation: calculus sequence, linear algebra, differential equations, probability and mathematical statistics, numerical methods and scientific computing.
• Computer science and data skills: programming (typically Python and/or R), data structures and algorithms, databases, software engineering basics, data visualisation and introductory machine learning for biological data.
• Core biology and experimental techniques: molecular and cellular biology, genetics, biochemistry, laboratory methods in molecular biology, and introductory ecology and physiology to provide biological context for computational work.
• Specialist computational biology modules: bioinformatics algorithms, sequence analysis, comparative genomics, structural bioinformatics, systems biology and network modelling, population genetics and statistical genomics.
• Applied and integrative experiences: a capstone project or senior thesis in computational biology, undergraduate research with faculty, and applied projects that may use real-world datasets from genomics, imaging or ecological studies.

Entry requirements

Applicants are expected to have a strong secondary-school preparation in both mathematics and the sciences. Admissions decisions at the University of San Diego are holistic and consider academic record, extracurricular achievements and personal qualities.

• Academic background: successful applicants typically present coursework in college-preparatory mathematics (including algebra and precalculus; completion of calculus is strongly recommended) and laboratory science (biology and chemistry are especially relevant).
• Recommended subjects: calculus, advanced mathematics where available, high-school biology, chemistry and physics. Prior programming experience is beneficial but not always required, as introductory programming is provided in the curriculum.
• Other factors: letters of recommendation, a personal statement that demonstrates interest in interdisciplinary quantitative biology, and evidence of analytical or research experience (for example, science fair projects, coding projects, or summer research) strengthen an application.

Career prospects

Graduates with this degree are equipped for roles that require both biological insight and quantitative/machine-learning skills. Career paths span industry, healthcare, government and academia.

• Industry roles: bioinformatics analyst, computational biologist, data scientist in biotechnology or pharmaceutical companies, and software engineer focused on life‑science applications.
• Healthcare and public sector: roles in clinical genomics laboratories, public-health informatics, and positions supporting diagnostic or precision-medicine initiatives.
• Research and academia: many graduates pursue graduate study (Master’s or PhD) in computational biology, bioinformatics, biostatistics or related disciplines and go on to research careers.
• Other opportunities: consulting, scientific data visualization and communication, regulatory science, and roles in startups or tech companies that work with biological data.

Why study at University of San Diego

The University of San Diego offers an undergraduate environment that emphasises interdisciplinary teaching, small class sizes and close faculty mentorship—beneficial for hands‑on training in computational biology. Students can take advantage of undergraduate research programmes and a senior capstone to build a portfolio of computational and experimental work.

• Interdisciplinary curriculum: deliberate integration of mathematics, computer science and life-science coursework prepares students to tackle real biological problems with quantitative approaches.
• Research and experiential learning: opportunities for faculty-mentored undergraduate research, independent projects and internships enhance practical skills and employability.
• Location advantages: proximity to a vibrant biotechnology and life-sciences ecosystem offers internship and collaboration opportunities with research institutes and companies in the region.
• Supportive community: small cohorts and accessible faculty allow personalised academic advising, support for graduate-school preparation, and pathways into local industry placements.