Kent State University

What you'll study

This master's programme combines core foundations in mathematics, statistics and computer science with applied topics in machine learning and large-scale data systems. Students follow a curriculum of advanced courses and a substantial culminating experience (thesis, practicum or project) that emphasises both theoretical understanding and practical implementation.

• Core topics: probability and mathematical statistics, linear algebra for data, numerical methods and optimisation.
• Computer science and engineering: algorithms for data, database systems, distributed and parallel computing, software engineering for data pipelines.
• Machine learning and analytics: supervised and unsupervised learning, deep learning, natural language processing, time series analysis and model evaluation.
• Big data technologies: scalable data storage and processing (batch and streaming), cloud computing concepts and practical tools for handling large datasets.
• Data visualisation and communication: techniques to present results, interactive visualisation and effective storytelling with data.
• Electives and special topics: courses may include advanced statistical modelling, computational biology, computer vision, high-performance computing, and domain-specific data science applications.
• Culminating experience: students typically complete a thesis, a client-focused practicum or an applied capstone project demonstrating integration of computational, statistical and engineering skills.

Entry requirements

Applicants are usually expected to hold a bachelor's degree from an accredited institution, preferably in computer science, engineering, mathematics, statistics, or a closely related quantitative discipline. Typical admissions considerations include undergraduate GPA, statement of purpose, letters of recommendation and a curriculum vitae or résumé.

• Academic background: prior coursework in calculus, linear algebra, probability or statistics, and programming (for example Python, R, C/C++ or Java) is normally required. Applicants without a directly related degree may be asked to complete prerequisite courses.
• Standardised tests: submission of standardized test scores is dependent on departmental policy; check the programme for current guidance.
• International applicants: evidence of English language proficiency is required, typically via recognised tests or approved institutional alternatives.
• Professional experience: relevant work experience can strengthen an application, particularly for applicants applying to project- or practicum-focused options.

Career prospects

Graduates enter a broad range of roles across industry, government and research. The programme prepares students for technical and leadership positions that require both computational expertise and statistical rigour.

• Data scientist or senior data scientist — building predictive models and deploying data-driven solutions.
• Machine learning engineer — developing, optimising and productionising ML systems.
• Data engineer or big data architect — designing and maintaining scalable data pipelines and infrastructure.
• Quantitative analyst or statistician — applying advanced statistical techniques in finance, healthcare or public policy.
• Research scientist or PhD candidate — continuing in academic or industrial R&D roles focused on methodological advances.
• Business intelligence and analytics roles — translating data insights into operational decisions across sectors such as healthcare, manufacturing, retail and government.

Why study at Kent State University

Kent State offers an interdisciplinary environment that brings together expertise from computer science, engineering and mathematical sciences. Students benefit from faculty who are active in applied research areas relevant to data science and access to institutional computing resources and research labs.

• Interdisciplinary teaching and research: coursework and projects draw on strengths across departments, enabling students to tailor their studies to computational, statistical or engineering emphases.
• Applied learning opportunities: capstone projects, practicum options and collaborations with local industries provide experience solving real-world problems.
• Facilities and resources: access to high-performance computing clusters, specialised software and research groups that focus on machine learning, big data and scientific computing.
• Regional and industry connections: proximity to Northeast Ohio industry and public-sector partners supports internships and employment pathways.
• Support services: dedicated graduate advising, career services and workshops help students prepare for technical interviews, portfolio development and professional networking.