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Columbia University’s Master of Science in Data Science (MSDS) is a prestigious, STEM-designated program that prepares students to become leaders in the design and application of data-driven systems. Administered by the Data Science Institute (DSI) and housed within The Fu Foundation School of Engineering and Applied Science, the program blends deep statistical theory with robust computational techniques and domain-specific expertise.
With access to world-class faculty, cutting-edge research, and New York City's rich tech ecosystem, Columbia MSDS students gain the skills to translate complex data into actionable intelligence for science, business, health, and public policy.
Program Format & Duration
Full-time (1.5 – 2 years) or part-time option (up to 5 years)
Includes a required capstone project, optional thesis, and internship support
Access to Columbia’s DSI research centers and industry affiliates
Core Curriculum Components
The MSDS program consists of 30 graduate-level credits, with required core, electives, and a capstone:
Core Courses
Probability and Statistics (e.g., STAT GR5203 or GR5204)
Algorithms for Data Science (CSOR W4246)
Machine Learning for Data Science (COMS W4771)
Exploratory Data Analysis and Visualization (STAT GR5701)
Data Science Capstone & Ethics (DATA GR5001 + DATA GR5999)
Technical Electives
Students choose from advanced electives in fields such as:
Deep Learning
Natural Language Processing
Time Series & Forecasting
Optimization & Convex Analysis
Statistical Inference & Bayesian Models
Big Data Systems (Hadoop/Spark)
Domain-Specific Electives
Students may specialize in applications across:
Business analytics
Computational biology
Public health
Urban analytics
Social science
Cybersecurity
Interdisciplinary course options are available from departments including Engineering, Public Health, Business, and SIPA (School of International and Public Affairs).
Columbia’s MSDS program places strong emphasis on applied learning, giving students direct exposure to real-world challenges through coursework, research, and industry collaborations.
Capstone Project
A cornerstone of the program, the capstone project is completed in teams and involves solving a real data science problem in partnership with an external client such as:
Tech firms (e.g., IBM, Meta, Bloomberg)
Startups
NGOs and government agencies
Research labs
Students work on tasks such as:
Developing predictive models
Automating decision systems
Designing dashboards and visualizations
Deploying ML applications in production environments
Research Opportunities
Students may engage with:
DSI Research Centers in topics like cybersecurity, health analytics, climate modeling, and fair AI
Faculty-led labs in Computer Science, Statistics, Biostatistics, or Engineering
Projects funded by NSF, NIH, or private industry partnerships
Students may also opt for a Master’s Thesis if interested in academic or research-focused careers.
Internships & Industry Exposure
The program offers:
Support for summer internships and part-time roles during the academic year
Access to career fairs, employer site visits, and networking events
Opportunities to work with Columbia’s NYC-based partners in finance, media, healthcare, and policy
Common internship destinations include: Amazon, Google, Two Sigma, Memorial Sloan Kettering, Bloomberg, UNDP, and startups in the Columbia Startup Lab.
Technical Tools & Platforms
Students gain proficiency in:
Python, R, SQL, Scala
TensorFlow, Keras, PyTorch
Hadoop, Spark, Dask
AWS, Azure, Google Cloud
Tableau, Power BI, Plotly, Git
Columbia’s MS in Data Science graduates are renowned for their technical excellence, analytical depth, and interdisciplinary fluency. They lead the transformation of industries by designing scalable, ethical, and human-centered data systems.
Career Outcomes
Common job titles include:
Hiring Industries
Graduate Study & Long-Term Paths
Some students choose to pursue:
Career Advancement
Salary Overview-
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What This Means for You-
Bachelor’s Degree: Hold a bachelor's degree from an accredited institution.Transcripts: Upload official transcripts from every post-secondary institution attended.Letters of Recommendation: Submit three letters of recommendation. Statement of Purpose / Personal Statement: Provide a statement that outlines your interest, background, and goals.Resume/CV: Include a current resume detailing education, skills, and relevant experience.GRE Scores: GRE General Test scores are accepted (often optional for recent cycles).English Proficiency: International applicants must submit TOEFL, IELTS, or equivalent unless waived.Application Fee: Pay the non-refundable application fee (currently $85).Three year degrees: Columbia accepts three‑year bachelor’s degrees for its M.S. in Data Science,WES: WES or another credential evaluation isn’t strictly mandatory, submitting one (especially a course-by-course report) is strongly recommended for international transcripts to ensure a thorough reviewData AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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