Cost & earnings at University of Missouri-Kansas City What students borrow here, and what they go on to earn
The Masters in Data Science at the University of MissouriKansas City prepares students to apply computational, statistical and engineering methods to large, complex data sets. It suits students with a quantitative background who want handson training in machine learning, data engineering and applied analytics for careers in industry, government or further research.
The programme combines core data science foundations with applied computational methods. Core topics typically include statistical inference and applied probability, machine learning, data mining, largescale data management and database systems, and scientific computing. Students also study supporting computational subjects such as algorithms, highperformance computing and software engineering for data applications.
Instruction emphasises practical skills: data cleaning and wrangling, feature engineering, model evaluation, data visualisation, and deployment of models on cloud and distributed platforms. Ethical issues in data science, reproducible research practices and data privacy are integrated into coursework.
Program structure usually offers a combination of required core courses, elective specialisms and a culminating experience. Common elective areas include deep learning and natural language processing, time series and forecasting, computer vision, data engineering and streaming analytics, and domain applications such as healthcare analytics or business intelligence. The degree is completed with either a capstone practicum or project, and some students may have an option to pursue a thesis or research project under faculty supervision.
Applicants are expected to hold a bachelorlevel degree from an accredited institution. A degree in computer science, engineering, mathematics, statistics, physics or another quantitative discipline is preferred. Candidates should have prior coursework or competence in programming (for example Python, R or Java), calculus and linear algebra, and introductory probability or statistics.
Typical application materials include official transcripts, a statement of purpose outlining your background and goals, a current résumé or CV, and letters of recommendation. International applicants must demonstrate English proficiency through an approved test unless otherwise exempted.
Applicants with strong quantitative aptitude but gaps in specific prerequisites may be admitted conditionally and asked to complete bridge or preparatory courses before taking advanced modules.
Graduates leave prepared for roles that require both algorithmic and practical data skills. Common career paths include:
Because the programme balances theory and applied experience, alumni work across sectors including technology companies, healthcare and life sciences, finance, government, and manufacturing, as well as in startups and research institutions.
UMKC offers a programme grounded in applied computational and engineering perspectives, with faculty who conduct research in machine learning, data systems and domain applications. The universitys location in Kansas City provides access to a growing regional technology ecosystem and employers in healthcare, logistics and finance, creating opportunities for internships, capstone projects and industry collaboration.
Students benefit from access to modern computing resources, interdisciplinary collaboration across engineering and business units, and flexible course scheduling that supports parttime study for working professionals. The programme emphasises handson learning and portfolio development, so graduates are prepared to contribute immediately to datadriven teams or to pursue further research.
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