University of Chicago

What you'll study

The programme emphasises applied methods and systems alongside foundational theory. You will take a mix of core and elective courses that build depth in areas such as machine learning, data systems, software engineering, operating systems and distributed systems, algorithms, and computational statistics. Typical modules include applied machine learning, data-intensive computing, advanced databases, cloud and distributed systems, software engineering principles, probabilistic modelling, and algorithms for large-scale data.

Instruction combines lectures, programming assignments, and project work. Many students complete a substantial capstone project or practicum in which they design, implement and evaluate a solution to a real problem—often in collaboration with industry partners, research labs or interdisciplinary teams. Research-led electives allow students to explore topics such as natural language processing, computer vision, privacy and security, and computational biology.

Entry requirements

Applicants are expected to hold a good bachelor’s degree in computer science, mathematics, engineering or a closely related discipline. Strong programming experience (in languages such as Python, Java, C++), demonstrated mathematical preparation (calculus, linear algebra, probability/statistics), and coursework in data structures and algorithms are typical prerequisites.

Applications normally include academic transcripts, a personal statement describing goals and relevant experience, letters of recommendation, and a CV. Professional experience or research experience in software development, data science or a related area is advantageous. Standardised tests may be optional or considered in context—check the programme’s admissions page for current testing policies. Applicants whose first language is not English will usually need to demonstrate English proficiency according to the university’s accepted tests.

Career prospects

Graduates move into a wide range of technical roles across industry and research. Common career outcomes include software engineer, data scientist, machine learning engineer, systems engineer, research scientist, and quantitative analyst. The programme’s applied orientation prepares students for roles that require building production systems, deploying models at scale, and collaborating across product and engineering teams.

Alumni take positions in technology companies, financial services, healthcare and biotech firms, and consulting organisations, as well as in research labs and startups. The combination of technical depth and applied project experience also supports students who wish to continue to PhD study in computer science or related fields.

Why study at University of Chicago

The University of Chicago offers a strong computer science department with active faculty research in systems, machine learning, theory, privacy, and computational biology, which you can draw on even in an applied master’s programme. The campus provides access to interdisciplinary centres and collaborations—such as partnerships with national laboratories and units focused on data science, economics, and public policy—creating opportunities to work on domain-specific computing problems.

Students benefit from a location in a major US tech and business hub, established career services, industry partnerships and a vibrant startup ecosystem. Coursework is supported by computing resources and opportunities for experiential learning through capstone projects, internships and collaborations with faculty and external partners.