University of Michigan

What you'll study

The programme covers core computational and statistical foundations together with applied skills for managing and analysing large-scale data. Typical topics include statistical learning and inference, supervised and unsupervised machine learning, applied Bayesian methods, optimisation, large-scale data systems and databases, high-performance computing, and data visualisation. Coursework commonly emphasises practical implementation in languages and tools such as Python, R, SQL and common machine-learning libraries, along with software engineering practices for reproducible research.

Students normally complete a mix of required core modules and elective courses that allow specialisation in areas such as natural language processing, computer vision, time series and streaming analytics, causal inference, and scientific computing. A substantial project component — often delivered as a capstone, practicum or research thesis — gives experience working on real-world datasets, typically in collaboration with faculty, research centres or external partners.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution, preferably in a quantitative or technical discipline such as computer science, engineering, mathematics, statistics, physics or a related field. Successful candidates normally demonstrate strong preparation in mathematics (calculus, linear algebra), probability and statistics, and programming experience (for example in Python, R, or similar).

Typical application materials include academic transcripts, a statement of purpose outlining research or career objectives, a curriculum vitae, and letters of recommendation. International applicants must demonstrate English language proficiency according to University of Michigan requirements. Admissions are competitive and decisions consider prior academic performance, relevant project or work experience, and fit with faculty and course offerings.

Career prospects

Graduates move into a wide range of technical roles where data-driven decision-making is central. Common career destinations include data scientist, machine learning engineer, data engineer, quantitative analyst, research scientist and analytics consultant. Alumni work across sectors including technology, finance, healthcare and life sciences, automotive and mobility, manufacturing, government and nonprofits.

The programme’s applied curriculum, capstone projects and industry connections help students build portfolios and secure internships or full-time positions. Graduates may also continue to doctoral study in fields such as computer science, statistics, or computational science and engineering.

Why study at University of Michigan

The University of Michigan offers a deeply interdisciplinary environment with faculty and research centres across computer science, statistics, engineering and the social sciences. Students gain access to resources such as advanced computing facilities, the Michigan Institute for Data Science and numerous applied research groups, enabling collaboration on domain-rich problems from healthcare to mobility and public policy.

Located in the Ann Arbor region, the University provides strong industry linkages, internship opportunities and a large alumni network. The university’s career services, technical seminars and organised practicum options support transition to industry or research careers, while a broad elective catalogue allows students to tailor the degree to specific technical interests or applied domains.