Columbia University

What you'll study

The Data Science bachelor's curriculum at Columbia emphasises a blend of theoretical foundations and hands‑on practice. Core topics include calculus and linear algebra, probability and mathematical statistics, data structures and algorithms, programming (typically Python and/or Java), databases and data management, machine learning, and data visualisation.

Beyond core units, students take courses in applied areas such as natural language processing, computer vision, time series analysis, optimisation, and large‑scale data systems. Coursework is often complemented by project‑based labs, a practicum or capstone project, and opportunities for research with faculty through the Data Science Institute and collaborating departments.

• Foundations: calculus, linear algebra, probability, mathematical statistics.
• Computing: programming, data structures, algorithms, databases, systems for big data.
• Modeling & analysis: machine learning, statistical inference, time series, optimisation.
• Applications & electives: NLP, computer vision, bioinformatics, social data analysis, econometrics.
• Practical experience: lab courses, capstone/project, internships, research assistantships.

Entry requirements

Admission to Columbia is highly selective and assesses the whole applicant. Typical academic preparation includes strong high‑school mathematics (calculus recommended), coursework in science and computer programming, and excellent overall grades. Applicants present a secondary‑school diploma or equivalent and submit transcripts, recommendation letters, and personal statements that demonstrate quantitative aptitude and intellectual curiosity.

Columbia evaluates applicants from diverse educational systems; international applicants provide proof of English proficiency where required. While standardised test policies can vary over time, candidates benefit from evidence of relevant experience such as programming projects, math contest participation, or coursework in statistics and computer science.

Career prospects

Graduates of Columbia's Data Science programme move into a wide range of roles across industry, government and academia. Common career paths include data scientist, machine learning engineer, data engineer, quantitative analyst, business intelligence analyst, and software developer. Alumni also pursue graduate study in statistics, computer science, machine learning, public policy, business, or domain fields applied to data science.

Because Columbia is located in New York City and connected to research centres, students frequently access internships and partnerships in finance, technology, healthcare, media, and public sector organisations. The programme's emphasis on projects and internships helps graduates enter roles that require both technical skills and the ability to communicate analytical insights.

Why study at Columbia University

Columbia offers a distinctive combination of a strong engineering school, interdisciplinary research initiatives and a major urban centre for industry. Students benefit from the Data Science Institute's collaborations across departments, access to faculty engaged in applied and theoretical research, and multidisciplinary coursework spanning computer science, statistics, business and domain sciences.

The university's New York City location provides extensive internship and networking opportunities with tech companies, financial institutions, healthcare organisations and media firms. Columbia also provides career services, student clubs, hackathons and research groups that support practical experience and professional development throughout the undergraduate years.