Virginia Commonwealth University

What you'll study

This interdisciplinary degree blends rigorous training in mathematics and computer science with foundational coursework in molecular and cellular biology. Early study emphasises calculus, linear algebra, differential equations, probability and statistics alongside introductory biology and chemistry. Students learn programming and software development skills relevant to biology, typically including Python, R, data structures and algorithms, and version control.

Core biomathematics and computational biology modules cover mathematical modelling of biological systems, statistical genomics, machine learning for life sciences, sequence analysis, comparative genomics, structural bioinformatics and systems biology. Laboratory or practicum components introduce molecular techniques and experimental design so students understand the provenance of biological data. The programme usually culminates in a capstone project or research placement in which students apply computational methods to real biological problems.

• Fundamental mathematics: calculus, linear algebra, differential equations, mathematical modelling
• Computing and data science: programming (Python/R), algorithms, data structures, statistics and machine learning
• Life sciences: cell and molecular biology, genetics, biochemistry, laboratory methods
• Specialist bioinformatics: sequence analysis, genomics, transcriptomics, structural bioinformatics, systems biology
• Applied experience: research project, internship or practicum with faculty or partner labs

Course structure and assessment

Students progress from general education and foundational STEM courses into increasingly specialised modules. Assessment typically combines problem sets, programming assignments, laboratory reports, exams and a final project or thesis. Elective options allow deeper focus on areas such as computational genomics, neuroinformatics, ecological modelling or biomedical data analytics.

Entry requirements

Admission to the undergraduate programme requires a secondary-school qualification equivalent to a U.S. high school diploma, with a strong background in mathematics (algebra and precalculus; calculus recommended) and science (biology and chemistry). Successful applicants usually demonstrate competence in mathematics and quantitative reasoning; prior programming experience is advantageous but not always mandatory.

Typical application materials include academic transcripts, a personal statement, and letters of recommendation. International applicants must meet English language proficiency requirements. Transfer students should expect review of college transcripts and may receive credit for appropriate prior coursework in mathematics, computer science or biology.

Career prospects

Graduates are prepared for roles that require both quantitative and biological expertise. Common career paths include bioinformatics analyst, computational biologist, data scientist in life sciences, genomic data analyst, biostatistician and software engineer for biotechnology companies. Graduates also pursue further study in specialised master’s or doctoral programmes in bioinformatics, computational biology, biostatistics, systems biology or medical and biomedical research programmes.

Employment settings range from academic and medical research centres to biotechnology and pharmaceutical companies, public-health agencies, clinical laboratories and companies that provide computational tools for genomics and personalised medicine. The combination of programming, statistics and biological knowledge also transfers to roles in healthcare analytics, regulatory science and scientific consulting.

Why study at Virginia Commonwealth University

VCU offers a programme grounded in interdisciplinary collaboration across mathematics, computer science and the life sciences, with close ties to VCU Health and research institutes in Richmond. Students benefit from access to active research laboratories, clinical partnerships and core facilities that support genomics, imaging and computational research. Faculty include researchers working on diverse problems such as cancer genomics, infectious disease modelling and biomedical data analysis.

The university’s urban location provides internship and networking opportunities with local biotech firms, hospitals and research institutes. Small-group research supervision, capstone projects and opportunities to contribute to publishable research prepare graduates for technical roles or further academic study. Support services such as career advising, computing resources and workshops in programming and data analysis help students develop the practical skills employers seek.