Columbia University

What you'll study

The programme combines advanced coursework with options for research, a project or a thesis. Core subject areas reflect Columbia's departmental strengths and typically include algorithms and theory, operating systems and distributed systems, databases and data management, machine learning and artificial intelligence, computer vision and natural language processing, networking, computer security and software engineering.

• Core and elective courses: students choose from rigorous graduate-level modules in algorithm design and analysis, advanced machine learning, deep learning, systems programming, cloud and distributed computing, database systems, compilers, and human–computer interaction.
• Research and project work: many students undertake a substantial design project or research thesis under faculty supervision, especially those planning to pursue research or doctoral study.
• Interdisciplinary options: opportunities exist to take courses or collaborate with units across Columbia — for example business, public policy, biomedical informatics and data science — to apply computing to broader domains.
• Capstone and practical experience: internships, industry-sponsored projects and practicum-based modules help students build applied skills and professional networks in New York City's tech ecosystem.

Entry requirements

Applicants should hold a recognised bachelor's degree in computer science, engineering, mathematics, physics or a closely related discipline, with strong quantitative preparation. Typical application materials required are official academic transcripts, a CV, a statement of purpose describing research and professional goals, and two or three academic or professional letters of recommendation.

• Academic background: coursework in programming, data structures and algorithms, discrete mathematics and calculus is normally expected; students without a CS degree may be considered if they can demonstrate equivalent preparation.
• English language: applicants whose first language is not English will need to demonstrate proficiency by an accepted test or other approved evidence, unless exempted by the university's policies.
• Other documentation: some applicants may submit GRE scores if they choose, and international applicants will need to provide documentation for visas and financial support as required by the university.

Career prospects

Graduates enter a broad range of technical and research roles across industry and academia. Common career paths include software engineer, systems engineer, data scientist or machine learning engineer, research scientist, site reliability engineer, security engineer and product manager for technical products. The programme also prepares students for doctoral study in computer science and related fields.

• Industry placements: the New York metropolitan area and strong alumni networks provide access to internships and roles at startups, established tech firms, finance and media companies and research labs.
• Entrepreneurship and innovation: students often leverage Columbia’s resources and local ecosystem to launch startups or join early-stage technology companies.

Why study at Columbia University

Columbia offers research-led teaching within a globally connected urban environment. Students benefit from access to world-class faculty, research centres in areas such as machine learning, systems and security, and collaboration opportunities across Columbia’s schools and New York City’s tech and research communities.

• Faculty and research: the department comprises active researchers whose work spans both theoretical foundations and applied systems, enabling students to engage in cutting-edge projects.
• Location and industry links: being in New York City gives students proximity to a dense concentration of technology companies, financial institutions and creative industries for internships and employment.
• Support and resources: students can access career services, entrepreneurship programmes, and alumni networks to support professional development and job placement.