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NYU’s Master of Science in Data Science (MSDS) is a highly competitive, STEM-designated graduate program offered by the Center for Data Science (CDS). The program is built on a rigorous foundation in statistics, machine learning, data engineering, and ethical AI, designed to prepare students for leadership roles in industry, research, and policy.
Situated in the heart of New York City and leveraging NYU’s interdisciplinary strength, the program blends deep technical skills with domain knowledge across business, health, tech, and public impact sectors. Students work alongside world-class faculty and researchers, including experts in natural language processing, deep learning, neuroscience, economics, and public health.
Program Format & Duration
Full-time (2 years); students typically complete 12 courses (32 credits)
Capstone or thesis track options available
Access to NYU-wide resources in computer science, AI, policy, and health
Core Curriculum Components
The MSDS program provides a strong theoretical and practical foundation in modern data science, with core coursework followed by domain-specific electives.
Core Courses (Required)
DS-GA 1001: Intro to Data Science
DS-GA 1002: Statistical and Mathematical Foundations
DS-GA 1003: Machine Learning
DS-GA 1004: Big Data or Scalable Data Systems
DS-GA 1006: Ethics of Data Science
Technical & Domain-Specific Electives
Students may choose from 40+ electives, such as:
Deep Learning
Natural Language Processing
Reinforcement Learning
Causal Inference
Computer Vision
Data Visualization
Bayesian Modeling
Data and Society
Health Data Science
FinTech Analytics
Electives are drawn from CDS, Courant Institute (Mathematics & CS), Stern School of Business, and other NYU graduate programs.
Capstone Project or Thesis (Optional)
Students complete either:
A Capstone Project with an external partner (startup, research lab, non-profit, government), or
An Independent Research Thesis under faculty supervision, often leading to publications or PhD preparation.
NYU’s MS in Data Science emphasizes applied, interdisciplinary learning through real-world projects, research collaborations, and access to NYC’s vast professional ecosystem.
Capstone Projects
Capstone teams work on real data challenges in collaboration with:
Tech companies (e.g., Google, Amazon, IBM, Flatiron Health)
Financial firms (e.g., Two Sigma, JPMorgan Chase)
Non-profits and UN-affiliated orgs
NYU research labs or NYC government departments
Projects focus on topics such as:
Predictive modeling and optimization
Fraud detection
Recommendation systems
Health analytics or medical imaging
NLP for social impact
Research Opportunities
Students collaborate with faculty across:
NYU Center for Data Science (CDS)
Courant Institute for Mathematical Sciences
NYU Grossman School of Medicine
NYU Wagner School (Public Policy)
NYU Langone Health, among others
Research areas include:
Interpretable AI
Fairness, accountability & transparency in algorithms (FATE)
Climate modeling
Computational neuroscience
Language technologies and multilingual NLP
Technical Tools & Skills
Students gain hands-on expertise in:
Python, R, SQL, Spark, Scala
TensorFlow, Keras, PyTorch
AWS, GCP, Azure
Docker, Kubernetes, Git
Hadoop, Airflow, Kafka
Tableau, Looker, Plotly
Professional Development & Industry Integration
The CDS career office provides:
1:1 mentorship and technical interview coaching
Employer networking events and data science career fairs
Workshops on resume, portfolio, and GitHub branding
Optional CPT and OPT pathways for international students
NYU’s MSDS graduates are recognized globally for their technical mastery, ethical insight, and domain versatility. Many go on to lead innovations in AI, tech, policy, health, and finance.
Career Outcomes
Typical roles include:
Hiring Industries
Graduate Study & Long-Term Paths
High-achieving students pursue:
Salary Overview-
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What This Means for You-
Bachelor’s Degree: Hold a bachelor’s degree from an accredited institution.Official Transcripts: Submit official transcripts showing degree conferral; upload them with your application.Three Letters of Recommendation: Provide three recommendation letters, preferably on official letterhead.Statement of Academic Purpose: Submit a statement outlining your academic interests, preparation, and career goals.Resume/CV: Include a current resume detailing your educational background, skills, and relevant experience.English Proficiency (if required): Provide TOEFL (recommended ≥ 100) or IELTS scores unless you qualify for an exemption.GRE Scores (Optional): GRE General Test submission is optional; no other standardized tests accepted. Educational Prerequisites: Demonstrated academic background in calculus, linear algebra, introductory programming (Python or R), and one advanced quantitative course (e.g., probability, statistics, or physics)Application Deadline: Submit all materials by early January (e.g., January 22, 2025, for Fall 2025)Application Fee: Pay the required application fee via the GSAS portalThree Year Degrees: NYU’s full M.S. in Data Science program requires a U.S.-equivalent four-year bachelor’s degree. WES: International applicants with a three-year degree or those from India, a course-by-course credential evaluation via WES (or another NACES-approved evaluator) is strongly encouraged and may be requiredData 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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