University of Michigan

What you'll study

The programme combines core foundations in statistics and machine learning with practical coursework in data acquisition, processing and communication. Typical modules cover:

• Statistical methods and inference – probability, hypothesis testing, regression and applied statistical modelling.
• Machine learning – supervised and unsupervised learning, model evaluation, regularisation and modern algorithms.
• Data engineering and big data – data pipelines, databases, distributed computing frameworks and scalable processing.
• Data visualisation and communication – principles of visual encoding, interactive dashboards and storytelling with data.
• Programming for data science – proficiency in languages and tools such as Python, R, SQL and common libraries for analysis and modelling.
• Ethics, privacy and policy – responsible use of data, fairness, interpretability and legal considerations.
• Capstone project or practicum – an applied, team-based project with real data where students solve a substantive analytics problem and present findings to stakeholders.

Students may also take elective courses from related areas such as optimisation, time-series analysis, natural language processing, computer vision, and domain-specific applications (healthcare, finance, public policy). The programme emphasises hands-on experience with real datasets, reproducible workflows and collaboration with faculty or external partners.

Entry requirements

Applicants are typically expected to hold a recognised undergraduate degree. Strong applicants demonstrate quantitative preparation through coursework or experience in calculus, linear algebra, probability/statistics and programming. Typical components of a successful application include:

• Official academic transcripts showing prior degrees.
• A CV or résumé outlining relevant technical experience and projects.
• A personal statement describing objectives, preparation and fit with the programme.
• Letters of recommendation from academic or professional referees who can attest to analytical ability and potential for graduate study.

Some programmes may request standardised test scores or require completion of specified prerequisite courses if an applicant’s background lacks sufficient quantitative training. Practical experience with coding, data analysis projects or workplace analytics is advantageous. English language proficiency evidence is required for applicants whose first language is not English.

Career prospects

Graduates move into a wide range of data-focused roles across sectors. Common career paths include:

• Data analyst or business analyst – analysing datasets to inform operational and strategic decisions.
• Data scientist – developing predictive models, experimentation frameworks and machine-learning solutions.
• Machine-learning or AI engineer – productionising models and building scalable predictive systems.
• Data engineer – designing and maintaining data infrastructure and pipelines.
• Analytics consultant or product analyst – translating analytical insights into business recommendations and product improvements.

The University of Michigan’s strong industry connections, career services and alumni network support recruitment into technology companies, financial institutions, healthcare organisations, consulting firms, startups and public-sector analytics teams. Many students also use the degree as preparation for doctoral study.

Why study at University of Michigan

The University of Michigan offers an interdisciplinary environment with access to leading faculty in statistics, computer science, business and domain areas. Students benefit from research centres and institutes focused on data science and analytics, opportunities for cross‑department collaboration, and extensive computing resources.

Located in Ann Arbor, the university provides strong links to industry and a vibrant innovation ecosystem, giving students access to internships, practicum partnerships and employer recruiting. Dedicated career support, active student communities and a large alumni network further enhance professional development and job placement opportunities.