Davenport University

What you'll study

This programme blends core mathematics and computing with molecular and cellular biology to provide the quantitative and practical tools needed for contemporary life‑science research. Expect progressive coursework in calculus, linear algebra and differential equations alongside probability and statistics for life sciences. Computer science modules focus on programming (typically Python and R), data structures, algorithms and databases, with specialised courses in bioinformatics, sequence analysis, genomics, proteomics and computational modelling of biological systems.

• Foundations: General biology, general chemistry, introductory genetics, and laboratory techniques to establish molecular life‑science knowledge.
• Mathematics & statistics: Calculus sequence, linear algebra, differential equations, mathematical modelling and applied statistics for experimental data.
• Computer science: Programming for biological data, data structures, algorithms, database management and scientific computing.
• Core computational biology: Bioinformatics methods, sequence alignment, phylogenetics, omics data analysis, machine learning applications in biology and systems biology.
• Capstone & experiential learning: A senior capstone project or research practicum working on a real dataset or modelling project, and opportunities for internships or co‑operative placements with industry or research groups.

Laboratory and data‑analysis labs run concurrently with lectures, emphasising reproducible workflows, command‑line tools, high‑performance computing basics and use of standard bioinformatics software. Electives may include advanced genomics, structural bioinformatics, health informatics or interdisciplinary projects with industry partners.

Entry requirements

Applicants should hold a recognised secondary school diploma or equivalent. Typical preparation includes high school mathematics through calculus or pre‑calculus, biology and chemistry. Admissions decisions consider overall academic record, strength of quantitative coursework, and any relevant experience such as coding, science clubs or laboratory work.

• Academic prerequisites: Mathematics (recommended: precalculus or calculus), biology and chemistry courses at secondary level.
• Standard documentation: Official transcripts; personal statement describing interest in quantitative biology; and letters of recommendation if requested.
• Transfers and mature applicants: Transfer credit is considered for college‑level mathematics, computing and science courses. Applicants with prior computing or lab experience are encouraged to highlight relevant work or project portfolios.

Career prospects

Graduates are prepared for roles that require quantitative analysis of biological data and computational modelling. Common career paths include bioinformatics analyst, computational biologist, data scientist in biotechnology or pharmaceutical companies, genomic data analyst, clinical bioinformatics specialist and laboratory informatics roles. Many graduates also move into research positions or pursue graduate study (master’s or PhD) in computational biology, bioinformatics, biostatistics or related disciplines.

Employers range from biotech and pharmaceutical firms to clinical laboratories, academic research centres, government agencies, health‑tech start‑ups and companies providing data‑driven health services. The programme’s emphasis on practical projects and internships supports direct entry into these sectors.

Why study at Davenport University

Davenport University emphasises career‑focused education with small class sizes and accessible faculty who have industry and applied research experience. The university’s approach mixes classroom instruction with practical labs, project work and opportunities for internships or co‑op placements that connect students with local and regional employers.

• Applied learning: Hands‑on laboratory and computing exercises, capstone projects and partnerships that give students portfolio work to show employers.
• Industry connections: Local business and healthcare relationships that support internships, guest lectures and collaborative projects.
• Student support: Dedicated career services, academic advising and resources to help with job placement or preparation for graduate study.
• Flexible delivery: Options for day, evening or blended courses to accommodate working students and those transferring credit.

Overall, the programme is designed to produce graduates who can bridge the gap between biological questions and quantitative solutions, ready for employment in the life‑sciences and data‑driven health sectors or for continued study.