University of Missouri-Kansas City

What you'll study

This master's combines core coursework in applied mathematics, statistics and computer science with specialised topics in molecular biology and bioinformatics. Students develop skills in algorithm design, statistical modelling, machine learning, sequence analysis, and the analysis of high-throughput biological data.

• Core quantitative modules: mathematical modelling for biological systems, statistical inference and regression methods, stochastic processes, numerical methods for differential equations.
• Computational and data science modules: algorithms and data structures for bioinformatics, machine learning for biological data, high-performance computing, programming for scientific computing (Python, R, or equivalent).
• Bioinformatics and computational biology modules: sequence analysis and comparative genomics, transcriptomics and RNA-seq analysis, proteomics and mass-spectrometry data analysis, structural bioinformatics.
• Laboratory and applied components: hands-on data analysis projects using real biological datasets, practical workshops in data preprocessing and reproducible research, and optional laboratory rotations or collaborations with UMKC biomedical research groups.
• Capstone or thesis: an applied project or research thesis supervised by faculty, typically involving development of new computational methods, statistical analysis of experimental data, or collaboration with an experimental laboratory.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, statistics, computer science, engineering, physics, biology with substantial quantitative coursework, or a closely related discipline. Typical preparation includes calculus, linear algebra, probability and statistics, and programming experience. Prior coursework in molecular biology or genetics is advantageous but not always required if the applicant demonstrates quantitative strength.

• Academic transcripts: a completed undergraduate degree from an accredited institution with strong performance in quantitative courses.
• Programming and mathematics background: evidence of programming ability (for example in Python, R, C/C++ or Java) and coursework in calculus, linear algebra and statistics.
• Supporting materials: statement of purpose outlining research or career goals, letters of recommendation, and a CV. Graduate entrance exam scores (e.g. GRE) may be optional or considered on a case-by-case basis according to departmental policy.
• International applicants: proof of English proficiency as required by the university if English is not the applicant’s first language.

Career prospects

Graduates enter a wide range of roles where quantitative analysis of biological data is essential. Career paths include positions in academic research groups, biotechnology and pharmaceutical companies, healthcare data science teams, public health agencies and bioinformatics service providers.

• Bioinformatics scientist or analyst in industry or academic core facilities
• Computational biologist developing models of biological systems or analysing omics data
• Biostatistician or data scientist working with clinical or genomic datasets
• Software engineer or developer focused on scientific tools and pipelines for biological data
• Continuation to PhD programmes in computational biology, biostatistics, systems biology or related fields

Why study at University of Missouri-Kansas City

UMKC offers an interdisciplinary environment with faculty across mathematics, computer science and the biological sciences who collaborate on computational biology problems. The programme emphasises hands-on data analysis, reproducible workflows and close faculty mentorship, providing opportunities for applied projects with local medical centres and research laboratories in the Kansas City biomedical community.

Students benefit from access to computational resources on campus and opportunities to take elective courses from related departments. The programme’s balance of theoretical foundation and applied training prepares graduates to contribute immediately in industry roles or to pursue further research training.