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Stanford’s Data Science major (housed under the School of Humanities & Sciences in collaboration with departments like Statistics, Computer Science, and Management Science & Engineering) provides a robust and flexible curriculum grounded in mathematics, computation, and real-world applications of data. The program emphasizes both theoretical foundations and hands-on skills across the data lifecycle.
Core Curriculum Components
Mathematical & Statistical Foundations
Math 51: Linear Algebra, Multivariable Calculus, and Modern Applications
Stat 116: Theory of Probability
Stat 191: Introduction to Applied Statistics
CS 109: Probability for Computer Scientists
Computational Foundations
CS 106A/B/X: Programming Methodology (Python or Java)
CS 107: Computer Organization and Systems
CS 161: Design and Analysis of Algorithms
CS 246: Mining Massive Data Sets (Advanced elective)
Core Data Science Modules
STATS 202: Data Mining and Analysis
STATS 203: Introduction to Regression Models and Forecasting
MS&E 226: “Analytics and Modeling” for optimization and decision-making
CS 229 or STATS 315A: Machine Learning (recommended advanced coursework)
Ethics & Societal Impact
STS 1 or PHIL 78: Introduction to Ethics in Technology
CS 182: Ethics, Public Policy, and Technological Change
Domain Electives (Choose from Application Areas)
Students select one or more focus areas such as:
Health and Biomedicine
Economics and Finance
Social Sciences
Environmental Data
Computational Humanities
Capstone & Integration
Senior Capstone Project: A data science research or industry-oriented project, involving a real-world dataset, statistical modeling, and communication of findings.
WIM (Writing in the Major): Typically fulfilled through a data science project-based course requiring a final report.
Stanford’s emphasis on real-world engagement ensures that Data Science majors gain practical experience applying their knowledge across research, industry, and interdisciplinary domains:
Undergraduate Research
Through programs like CURIS (for CS research) and UAR (Undergraduate Advising and Research), students engage in faculty-mentored research in AI, computational biology, economics, sustainability, and more. Students contribute to published papers and conference presentations early in their undergraduate careers.
Capstone Projects & Practicums
Capstone courses involve solving real-world problems for nonprofits, research labs, or Silicon Valley tech firms. Students manage data pipelines, apply machine learning, and deliver client-ready solutions, often in teams.
Interdisciplinary Labs
Students collaborate with labs such as:
Stanford AI Lab (SAIL)
Human-Centered AI Institute (HAI)
Biomedical Data Science Lab
Center for Data for Good
These offer platforms for students to explore the intersection of data science with ethics, neuroscience, public health, and sustainability.
Internships & Industry Connections
Stanford’s Silicon Valley proximity ensures unparalleled access to internships with tech giants, high-growth startups, and global think tanks. Students regularly intern as:
Data scientists at Google, Meta, and LinkedIn
Research interns at Microsoft Research, NASA, and federal agencies
Strategy and AI interns at consultancies and social impact organizations
Hackathons & Competitions
Students participate in:
Stanford TreeHacks (flagship hackathon)
Datafest competitions
Kaggle challenges
Ethics in AI case competitions
These sharpen technical, ethical, and collaborative problem-solving.
Graduates of Stanford’s Data Science program are well-positioned for elite graduate programs, high-impact professional roles, and leadership in both technology and policy-driven organizations.
Career Trajectories Include:
Industry Leaders: Roles in AI product development, data engineering, cloud analytics, or product strategy at companies like Google, Amazon, Apple, Palantir, and Stripe.
Public Sector & Research: Roles with the World Bank, UN, CDC, National Institutes of Health, and think tanks using data for social good.
Advanced Studies: Many pursue MS or PhDs in:
Data Science
Computer Science
Statistics
Computational Biology
Public Policy/Data Ethics
Business Analytics (including deferred MBA options)
Long-Term Pathways
Data Scientist → Lead Data Scientist → Chief Data Officer
Machine Learning Engineer → AI Architect → Head of AI
Product Analyst → Product Manager → Head of Data Products
Research Analyst → Policy Director → Data Governance Leader
Graduates are equipped not only with technical mastery but also with ethical awareness, critical thinking, and interdisciplinary fluency—traits increasingly essential in a world where data shapes every domain of human activity.
Deadline Details: If you intend to submit an Optional Arts Portfolio, your application must be submitted by October 15 for Restrictive Early Action (REA) or December 5 for Regular Decision (RD). Your School Report form, teacher/counselor letters of recommendation, and transcript(s) can be submitted by November 1 (REA) or January 5 (RD).SAT/ACT Update: Beginning with the 2025–2026 application cycle, Stanford will reinstate its standardized testing requirement. ACT or SAT scores will be required for first-year and transfer students submitting applications for the Fall 2026 entry term.SAT/ACT Score Reporting: If you choose to submit ACT/SAT test scores as part of your application, we recommend that you simply self-report your highest scores in the testing section of the application. You can also have official scores sent to Stanford, but this is not required for us to review your application.SAT/ACT Super Score & Codes: For the ACT, you may report the calculated ACT superscore in addition to your highest section scores. For the SAT, we will superscore, focusing on the highest individual Evidence-Based Reading and Writing and Math scores from all test sittings. SAT PROFILE/TOEFL Code number: 4704, ACT Code number: 0434English Language Proficiency Requirements: No, we do not require any English proficiency exams. While these exams (TOEFL, IELTS, Duolingo English Test, etc.) are useful in helping us determine our applicants’ English proficiency, you may feel your fluency will be clear in other aspects of your application. We have no preferred proficiency exam. Generally, we receive English proficiency exam results from applicants whose native language is not English and/or the primary language of instruction at their secondary school is not English. Please note that fluency in English is a prerequisite for admission to Stanford.Recommendations: School Report- Required, Counselor Recommendation- Required, Mid Year Report- Required, Final Report- Required, Teacher Evaluation(s): 2 Required, 0 Optional, Other Evaluation(s): 0 Required, 1 Optional.Portfolio Requirements: If your application is submitted by the applicable early deadline, October 15 (REA) or December 5 (RD), you will be sent an email with instructions to access your Stanford portal within 48 hours of Stanford receiving your application. To submit the Arts Portfolio: 1. Log in to your Stanford portal, scroll below your checklist to the "Optional Arts Portfolio" section, and submit the Arts Portfolio form. 2. After submitting the Arts Portfolio form, use the link that appears to upload your Arts Portfolio materials by the applicable early deadline, October 20 (REA) or December 10 (RD). Applicants may submit a portfolio in just one arts area. Portfolios must follow the exact Arts Portfolio Requirements detailed on our website.Letter of Recommendation: When assigning recommenders in the Recommenders and FERPA section, please review the Letters of Recommendation section on the Transcript and Required School Forms webpage.Writing Requirements: Common App personal essay- Required, College questions- 8 Required Questions. Graduates of Stanford’s Bachelor of Science in Data Science program are equipped with deep analytical thinkingcomputational skillsand domain-specific knowledge—making them well-prepared for high-demand roles across sectors that increasingly depend on data to drive decisionsinnovationand strategyData & AI Engineering
Data Scientist: Extracting insights from large-scalecomplex data sets to guide strategyMachine Learning Engineer: Designing scalable algorithms for learning and predictionData Engineer: Architecting and managing data pipelines and infrastructureApplied Statistician: Developing statistical models for evidence-based insightsAI Engineer: Implementing data-powered intelligent systemsBusiness & Strategy
Data Analyst: Interpreting business data to influence operational and strategic decisionsBusiness Intelligence Analyst: Delivering dashboards and visual analytics toolsProduct Analyst (Tech Focus): Evaluating user data and product performanceOperations Analyst: Improving efficiency through data-driven process optimizationAI Product Manager: Leading development of data-focused tech solutionsFinance & Risk Analytics
Quantitative Analyst: Using data science to model financial systems and investment risksRisk Modeler: Predicting business and market risk with advanced data toolsFinTech Analyst: Creating data-driven financial products and algorithmsInvestment Strategist (Data Focus): Guiding investments using predictive analyticsPolicyHealth & Social Impact
Public Policy Analyst (Data Focus): Using data to inform and assess policy impactsHealth Data Scientist: Improving public health outcomes through data insightsSocial Impact Analyst: Applying data to address inequalitysustainabilityand global developmentBehavioral Data Analyst: Modeling and analyzing societal behavior patternsEntrepreneurship & Innovation
Data-Driven Startup Founder: Launching ventures based on innovative data solutionsInnovation Strategist (Data Focus): Identifying market gaps and building solutionsAI Ethics & Governance Specialist: Tackling the ethical challenges of data use
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