Loyola University Chicago

What you'll study

The curriculum combines foundational courses in mathematics, computer science and biology with specialised modules in computational biology and bioinformatics. Students progress from calculus, linear algebra and probability to courses in statistics and scientific computing, while concurrently taking cell and molecular biology, genetics and biochemistry to ground computational work in biological context.

• Core mathematics and computation: calculus sequence, linear algebra, differential equations, probability and mathematical statistics, numerical methods, data structures and algorithms, and scientific programming (Python, R, or equivalent).
• Core biological sciences: general biology, cell and molecular biology, genetics, biochemistry and laboratory methods to provide experimental context for computational analyses.
• Specialist computational biology modules: sequence analysis and alignment, genome informatics, next‑generation sequencing data analysis, structural bioinformatics, systems biology and network modelling, machine learning for life sciences, and computational genomics.
• Applied and experiential learning: laboratory classes, computational practicum, project courses, internships with research groups or industry partners, and a senior capstone or honours thesis involving an independent computational research project.
• Electives and breadth: courses in data visualisation, high‑performance computing, ethics of biotechnology, biostatistics, and complementary electives from departments such as neuroscience, chemistry or public health.

Teaching methods include lectures, problem‑solving workshops, programming laboratories and independent research under faculty supervision. The programme emphasises reproducible computational practice, data stewardship and the biological interpretation of quantitative results.

Entry requirements

Applicants are expected to have completed a high school diploma or equivalent with a strong background in mathematics and science. Typical preparation includes:

• High school mathematics through calculus if available, or two years of advanced mathematics such as pre‑calculus and calculus.
• High school biology and chemistry; an introductory course in computer science or programming is strongly recommended.
• A competitive academic record demonstrated by transcripts; standardised tests may be considered according to the university's admissions policy.
• Personal statement describing interest in quantitative biology and any relevant laboratory or coding experience; letters of recommendation are often required.

Transfer applicants with college coursework in calculus, programming and introductory biology are welcome and their previous credits are evaluated for placement. International applicants must meet the university's requirements for academic records and English language proficiency.

Career prospects

Graduates are prepared for roles that require quantitative and computational fluency applied to biological data. Common career paths include:

• Bioinformatics analyst or specialist in academic, healthcare or industry laboratories, where graduates analyse sequencing and omics datasets.
• Computational biologist or data scientist in biotechnology and pharmaceutical companies, working on drug discovery, biomarker identification and translational research.
• Positions in clinical and public health informatics, medical technology firms, and companies providing data services to life‑science sectors.
• Further study at graduate level (MSc or PhD) in computational biology, bioinformatics, biostatistics or related fields, and professional pathways such as medical or dental school.

Hands‑on research projects, internships in the Chicago biomedical ecosystem, and training in coding and statistics enhance employability and readiness for advanced study.

Why study at Loyola University Chicago

Loyola offers an interdisciplinary and student‑centred environment with opportunities for close mentorship from faculty active in computational and life‑science research. The university’s emphasis on undergraduate research enables students to join labs and participate in projects that combine wet‑lab and computational methods.

• Interdisciplinary culture: strong collaboration across departments — mathematics, computer science, biology and health sciences — supports tailored study plans and cross‑disciplinary projects.
• Research and experiential opportunities: students can engage in faculty‑mentored research, internships and practicum placements in the Chicago area’s research hospitals, biotech companies and public health organisations.
• Jesuit education values: the programme integrates ethical reflection with technical training, preparing graduates to consider the societal implications of computational work in biology and medicine.
• Preparatory support: academic advising, career services and workshops in coding, statistics and scientific communication help students transition to careers or graduate study.

Overall, Loyola’s programme is well‑suited to students who want rigorous quantitative training grounded in biological science, combined with practical research experience and ethical preparation for careers in computational biology and related fields.