University of Michigan

What you'll study

The programme builds from a foundation of calculus, linear algebra and introductory programming into specialised courses in probability, statistical inference, machine learning and data engineering. Early coursework typically covers computing fundamentals (Python and R), data structures and algorithms, and mathematical tools for data analysis. Core modules introduce statistical modelling, supervised and unsupervised learning, data visualisation, databases and data wrangling.

Upper-level study emphasises applied projects and domain specialisation: students select electives in areas such as natural language processing, computer vision, time series, Bayesian methods, high-performance computing, and ethics and policy for data science. The curriculum normally culminates in a capstone or practicum project where students work on real datasets—often in collaboration with faculty research groups, industry partners or campus institutes—to design, implement and evaluate end-to-end data solutions.

• Foundations: calculus, linear algebra, discrete mathematics
• Computing: programming, data structures, software engineering practices
• Statistics and probability: inference, regression, experimental design
• Core data science: machine learning, databases, data visualisation, data engineering
• Advanced electives: NLP, computer vision, scalable data systems, Bayesian modelling
• Capstone/practicum: team-based applied project with dataset acquisition, modelling and communication

Entry requirements

Admission is competitive and based on academic achievement from secondary education. Typical successful applicants present strong performance in mathematics (including calculus where available) and evidence of quantitative aptitude. Previous programming experience is advantageous but not always required; demonstrated problem-solving skills through coursework, competitions or personal projects is helpful.

Applicants are also assessed on the strength of their overall curriculum, recommendations, and personal statements that explain interest in data science. Transfer students are considered and should present college-level coursework in relevant subjects. International applicants must meet the university's general admissions and English language proficiency expectations.

Career prospects

Graduates enter a broad set of roles where data-driven decision-making is central. Common career paths include data scientist, data analyst, machine learning engineer, data engineer, business analyst and research assistant. Alumni work across industries such as technology, finance, healthcare, automotive, public policy and consulting.

The degree also provides a strong foundation for graduate study in statistics, computer science, machine learning and related fields. Graduates frequently take roles that combine technical work with domain expertise, engage in product development, or pursue research and advanced degrees.

Why study at University of Michigan

The University of Michigan offers an interdisciplinary environment with strong collaboration between departments such as Statistics, Computer Science & Engineering, and the School of Information. Students benefit from access to campus research centres and institutes focused on data science and analytics, which provide opportunities for undergraduate research and industry partnerships.

Ann Arbor’s vibrant tech and startup community, together with extensive career services and employer recruiting, supports internship and employment opportunities. On campus, students can join student groups and project teams to gain hands-on experience and build portfolios. The programme emphasises practical, ethical and scalable approaches to data problems, preparing graduates to contribute in diverse professional and academic settings.