University of Michigan

What you'll study

The programme blends core computer science subjects with information science perspectives. Core areas typically covered include algorithms and data structures, machine learning, databases and information retrieval, operating systems and distributed systems, and software engineering. From the information sciences side, students study human–computer interaction, information architecture, data visualisation, privacy and information policy, and qualitative methods for information research.

Teaching mixes lectures, graduate seminars, hands-on programming and laboratory work. Students commonly complete a capstone project or practicum that partners with industry, campus research groups or community organisations. Elective options allow focus in areas such as:

• Data science and analytics (advanced machine learning, statistical modelling, big data systems)
• Human–computer interaction and UX research (user-centred design, prototyping, usability testing)
• Systems and infrastructure (cloud computing, distributed systems, security)
• Information policy, ethics and governance (privacy, fairness, socio-technical analysis)

Many students also take research-oriented courses and may undertake an independent thesis under faculty supervision if they are considering doctoral study.

Entry requirements

Applicants are expected to hold a bachelor’s degree or equivalent. A degree in computer science, information science, engineering, mathematics, statistics or a related quantitative discipline is typical; applicants from other backgrounds may be considered if they can demonstrate sufficient programming and quantitative preparation.

Standard components of a competitive application include:

• Academic transcripts showing strong performance in relevant courses (programming, algorithms, mathematics)
• A statement of purpose describing academic and professional goals, and fit with the programme
• Letters of recommendation from academic or professional referees who can speak to your technical and research potential
• A CV or résumé detailing relevant coursework, projects, internships and work experience
• Proof of English proficiency for applicants whose first language is not English (such as TOEFL or IELTS), where required

Some applicants may be asked to demonstrate coding ability through coursework, portfolios, or technical work samples. GRE requirements vary by unit and may be optional; check the specific programme page for current testing policy.

Career prospects

Graduates move into a wide range of technical and interdisciplinary roles across industry, government and research. Common job titles include software engineer, data scientist, machine learning engineer, systems engineer, UX researcher/designer, information architect, product manager and information analyst. The combination of computing and information skills also prepares graduates for roles in privacy and policy teams, digital accessibility, and technical consultancy.

Alumni find positions in technology companies, start-ups, healthcare and finance sectors, as well as in public-sector and non-profit organisations. Many graduates also pursue further research by enrolling in PhD programmes in computer science, information science or related fields.

Why study at University of Michigan

The University of Michigan offers an interdisciplinary environment where engineering, computer science and information studies intersect. Students benefit from access to a broad base of faculty expertise, cross-school collaborations, and research centres focused on data science, AI and socio-technical methods. The campus’s strong industry links and proximity to a vibrant tech ecosystem support internship and employment opportunities.

The university emphasises experiential learning; students can collaborate on large-scale research projects, engage in community-facing practicum work, and take advantage of career services and an extensive alumni network. Facilities include modern computing laboratories and opportunities to work with faculty on cutting-edge applied and theoretical problems in computing and information.