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The Master of Science in Engineering in Data Science (MSE-DS) at the University of Pennsylvania is an interdisciplinary, STEM-designated graduate program housed within Penn Engineering, in collaboration with departments such as Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Statistics and Data Science at Wharton.
The program offers a rigorous foundation in computational algorithms, statistical inference, machine learning, and data systems, preparing students to solve complex problems across sectors like finance, healthcare, technology, and public policy. It is ideally suited for applicants with strong quantitative and programming skills looking to pursue technical careers in data engineering, analytics, AI, or research.
Program Format & Duration
Full-time, on-campus program
Duration: 1.5–2 years (flexible depending on course load)
STEM-designated (eligible for up to 3 years OPT)
Entry: Fall intake only
Administered by Penn Engineering, with cross-registration access to Wharton and other Penn schools
Core Curriculum Components
The curriculum combines rigorous theoretical foundations with practical application and flexibility to specialize through electives.
Core Courses
Mathematics and Foundations
Probability and Statistics for Data Science
Linear Algebra and Optimization
Data Structures and Algorithms
Computational and Analytical Core
Machine Learning
Artificial Intelligence
Data Mining
Statistical Learning Theory
Data Engineering and Systems
Big Data Systems
Cloud Computing and Databases
Scalable Data Analytics Platforms
Applied Analytics and Ethics
Data Ethics and Policy
Visual Analytics and Storytelling
Business Applications (elective via Wharton)
Electives and Customization
Students can tailor their learning with electives in:
Biomedical Data Science
Robotics and IoT
Computational Social Science
Finance and Econometrics
NLP and Computer Vision
Students may also take up to 3 courses outside Penn Engineering, including from Wharton, School of Medicine, or the School of Arts and Sciences.
Penn's MSE-DS emphasizes hands-on learning through research, projects, and access to real-world datasets and faculty-led labs.
Capstone / Independent Study
Students can undertake:
A research-based capstone with a faculty advisor
An industry-focused applied project (especially for those targeting product roles)
Topics may include:
AI for healthcare
Predictive analytics in finance
Deep learning for imaging or NLP
Research Opportunities
Students may collaborate with:
Penn Research in Machine Learning (PRiML)
Center for Health Informatics and Analytics
Wharton Customer Analytics Initiative
GRASP Lab (robotics and perception)
Industry Connections
Located in Philadelphia, with close access to NYC and DC
Penn’s strong alumni and corporate network support internships and applied experiences
Career services assist with interview prep, networking, and industry mentorship
Graduates from Penn’s MSE-DS program are positioned to lead in data-driven roles across academia, research, and industry.
Career Outcomes
Typical job titles include:
Data Scientist
Machine Learning Engineer
Quantitative Analyst
AI/ML Researcher
Data Product Manager
Computational Researcher
Top Employers
Alumni have joined:
Google, Meta, Microsoft
JPMorgan, BlackRock, Capital One
Johnson & Johnson, Merck
Startups in fintech, healthtech, and AI
Research institutions and government labs
Further Study
Graduates are also well-prepared for:
PhD programs in Computer Science, Statistics, or AI
MBA or MPA programs for data-driven leadership
Applied research roles in healthcare, public policy, and environmental science
Bachelor’s Degree & Major GPA: Hold a bachelor’s degree with a strong quantitative background and a minimum major GPA of 3.4, including A− or better in key courses Prerequisite Coursework: Complete with B+ or higher in foundational subjects: programming (e.g., CIS 1200/CIT 5900), data structures/algorithms, probability/statistics (e.g., CIS 5450 or STAT 510), and additional math/programming as relevantTranscripts: Upload academic transcripts (unofficial at application, official upon admission) from all institutions attendedResume/CV: Provide a current résumé detailing education, research, and technical experiencePersonal Statement: Submit a statement explaining your interest in Data Science and how your background aligns with the program—and, for dual/accelerated applicants, why you want that integrated pathwayReferences: List two professional references (letters may be requested later), typically from faculty or supervisorsGRE Scores: GRE is optional; strong quantitative scores may strengthen your profile English Proficiency: International applicants must meet Penn’s English requirements via TOEFL/IELTS or equivalentDeadlines: Applications are reviewed twice a year: Accelerated/dual master’s by May 15 and December 15; standard master’s typically for Fall entry, review timelines approximate mid‑June or mid‑JanuaryThree‑Year Bachelor’s Degrees: Applicants must hold a U.S.-equivalent bachelor’s degree, and those with three-year degrees should verify their eligibility with the admissions office as it’s not automatically accepted .WES Evaluation: Applicants from non-U.S. institutions are required to submit a NACES-approved course-by-course credential evaluation (e.g. WES, ECE) sent directly to Penn Engineering Graduate Admissions Data 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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