Case Western Reserve University

What you'll study

This interdisciplinary bachelor’s programme integrates mathematics, statistics, computer science and molecular and cellular biology. Core coursework typically includes calculus and multivariable calculus, linear algebra, probability and statistics, numerical methods, and mathematical modelling alongside foundational biology courses such as cell and molecular biology, genetics, biochemistry and introductory laboratory techniques.

On the computational side, students study programming (often Python and/or R), data structures and algorithms, databases, algorithms for bioinformatics, machine learning and computational genomics. Practical modules emphasise biological data analysis, sequence alignment, transcriptomics, structural bioinformatics and network biology.

Hands-on components include laboratory courses, computational laboratories, and project-based classes. Many students complete a capstone or senior thesis project that applies quantitative methods to a biological question. Electives and special topics allow deeper focus in areas such as systems biology, population genetics, structural biology, image analysis, or clinical informatics.

• Typical modules: Calculus; Linear Algebra; Probability & Statistics; Numerical Analysis; Programming for Scientists; Data Structures & Algorithms; Molecular Biology; Genetics; Biochemistry; Bioinformatics Algorithms; Computational Genomics; Machine Learning & Data Mining; Laboratory Practicum; Senior Capstone/Research Project.
• Structure: The degree combines a sequence of mathematics and computer science prerequisites with biology and laboratory requirements, culminating in advanced electives and an independent research or capstone experience.
• Research & practical experience: Strong emphasis on undergraduate research, computing projects, and opportunities to work in faculty labs or in collaboration with clinical and industry partners.

Entry requirements

Admission is through Case Western Reserve University’s undergraduate admissions process. Successful applicants typically present a strong high-school academic record with emphasis on quantitative and science subjects. Recommended preparation includes high school mathematics through calculus, biology, chemistry and prior experience with computer science or programming if available.

Typical evidence of readiness includes rigorous coursework (e.g. honours-level or advanced placement subjects), strong grades in STEM subjects and demonstrated interest in interdisciplinary problems through projects, extracurriculars or research. Because admissions standards and procedures evolve, applicants should consult the university’s admissions office for the most current guidance.

Career prospects

Graduates are prepared for a range of roles that require expertise in analysing biological data and building computational models. Common career paths include bioinformatics analyst, computational biologist, data scientist in biotechnology or pharma, genomic data analyst, software developer for scientific applications, and roles in healthcare analytics or clinical informatics.

Many graduates continue to postgraduate study (master’s or PhD) in computational biology, bioinformatics, biostatistics, systems biology, or related fields, or pursue professional degrees in medicine or allied health. Undergraduates with experience in programming and laboratory work are also competitive for industry internships and research positions.

Why study at Case Western Reserve University

Case Western Reserve offers an interdisciplinary environment where departments of mathematics, computer science and biology collaborate closely. The university’s location and partnerships provide notable advantages: proximity to world-class clinical and research institutions in Cleveland, collaborative opportunities with nearby hospitals and research centres, and access to modern laboratory and computing facilities.

Undergraduate students benefit from a strong research culture with faculty-led labs open to undergraduates, formal honours and capstone options, and support for internships and co-curricular experiences. The programme’s combination of quantitative rigour and biological training is designed to produce graduates who can move seamlessly between computational analysis and experimental biology.