Kettering University

What you'll study

This interdisciplinary programme blends rigorous mathematics and computer science with core biological sciences. In early years you cover foundational subjects such as calculus, linear algebra, differential equations, probability and statistics, introductory and organic chemistry, cell and molecular biology, and introductory programming.

As you progress, course work becomes specialised and application-focused. Typical modules include:

• Computational Methods and Programming: Python, R, data structures and algorithms, scientific computing and software engineering practices for reproducible research.
• Statistical and Mathematical Modelling: biostatistics, stochastic processes, dynamical systems and numerical methods for modelling biological phenomena.
• Bioinformatics and Genomics: sequence analysis, alignment algorithms, next-generation sequencing data processing, comparative genomics and functional annotation.
• Systems and Computational Biology: network analysis, systems modelling of cellular pathways, agent-based models and multiscale simulation.
• Molecular and Cellular Biology Laboratory: molecular techniques, experimental design, data acquisition and quality control that complement computational analysis.
• Machine Learning and Data Science for Biology: supervised and unsupervised methods, feature extraction for biological data, and applications to biomarker discovery and image analysis.
• Capstone and Research Experience: team-based design projects, independent research with faculty on applied problems such as predictive modelling, drug target analysis or clinical data analytics.

The curriculum emphasises hands-on experience with real biological datasets, use of high-performance and cloud computing resources, and integration of wet-lab and dry-lab perspectives. Co-operative education and industry project courses are embedded to provide workplace experience.

Entry requirements

Applicants should demonstrate strong preparation in mathematics and the natural sciences. Typical expectations include:

• High school diploma or equivalent with strong grades in mathematics (calculus or pre-calculus recommended), biology and chemistry.
• Demonstrated competence in algebra, trigonometry and an introduction to calculus. Prior exposure to statistics and programming is advantageous but not always required.
• For international applicants, proof of secondary school credentials equivalent to U.S. high school graduation; English language proficiency where applicable.
• Admissions review considers academic record, recommendations and, where required, an interview or additional materials that demonstrate quantitative aptitude and motivation for interdisciplinary work.

Prospective students transferring from other institutions should provide transcripts and descriptions of prior coursework in mathematics, computer science and biology to evaluate transfer credit.

Career prospects

Graduates leave prepared for a wide range of roles that combine quantitative skills with biological knowledge. Common career paths include:

• Bioinformatics analyst or computational biologist in biotechnology and pharmaceutical companies, working on genomics, proteomics and drug discovery data.
• Data scientist or machine learning engineer applying predictive models to clinical, epidemiological or imaging datasets.
• Biostatistician supporting clinical trials, public health studies or regulatory submissions.
• Research or technical positions in academic laboratories and government research institutes, often as a precursor to graduate study.
• Software developer for scientific tools, databases and bioinformatics pipelines.

Graduates are also well placed to continue to postgraduate study (Master’s or PhD) in computational biology, bioinformatics, biostatistics, systems biology or related fields.

Why study at Kettering University

Kettering is known for its experiential learning model and extensive co-operative education programme, which integrates paid work terms with academic study. Students in this programme benefit from alternating academic terms and industry placements that build practical skills and professional networks.

The university offers small class sizes, close faculty mentorship and access to computing infrastructure as well as laboratory facilities where students can combine wet-lab experiments with computational analysis. Strong ties with regional industry, research institutions and healthcare organisations provide opportunities for internships, capstone projects and collaborative research.

Overall, the programme is designed to produce graduates who are immediately employable in data-driven life sciences roles or who are ready for further study, with an emphasis on applied skills, interdisciplinary problem solving and professional experience.