Data Science, MSE
Penn’s Master of Science in Engineering (MSE) in Data Science prepares students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications.
The Data Science Program can typically be completed in one-and-a- half to two years. It blends leading-edge courses in core topics such as machine learning, big data analytics, and statistics, with a variety of electives and an opportunity to apply these techniques in a domain specialization of choice.
The domain specialization offers both preparatory coursework and a thesis or practicum in a data science application area. Potential areas of specialization include network science (the Warren Center for Network and Data Science), digital humanities (the Price Lab for Digital Humanities), biomedicine (the Institute for Biomedical Informatics), and public policy (the Penn Wharton Budget Model and the Annenberg Center for Public Policy) — as well as more traditional opportunities in Computer and Information Science and Electrical and Systems Engineering. For students interested in applying data analysis and modeling to other areas within engineering and the physical sciences, Penn offers a specialized and synergistic program in Scientific Computing.
For more information: https://dats.seas.upenn.edu/program/
For students interested in learning more about MSE-DS Online, click here.
Curriculum
10 course units are required for the Data Science degree.1
| Code | Title | Course Units |
|---|---|---|
| Foundations (2 cu's) | ||
| CIT 5900 | Programming Languages and Techniques | 1 |
| or CIT 5910 | Introduction to Software Development | |
| Select one of the following: | ||
| CIS 5150 | Fundamentals of Linear Algebra and Optimization | 1 |
| or MATH 5130 | Computational Linear Algebra | |
| Core Requirements (3 cu's) | ||
| ESE 5420 | Statistics for Data Science | 1 |
| CIS 5450 | Big Data Analytics | 1 |
| Select one of the following: | ||
| CIS 5190 | Applied Machine Learning | 1 |
| or CIS 5200 | Machine Learning | |
| or STAT 5710 | Modern Data Mining | |
| or ENM 5310 | Data-driven Modeling and Probabilistic Scientific Computing | |
| or ESE 5450 | Data Mining: Learning from Massive Datasets | |
| Technical Electives (5 cu's) | ||
| Students must choose from at least 3 of the buckets listed below | 5 | |
| Total Course Units | 10 | |
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The ten course units for the Data Science degree are divided into three categories: Foundations, Core Requirements and Technical Electives. (As long as the prerequisites for the courses are met, students can complete these courses in any sequence)
Technical Electives1
| Code | Title | Course Units |
|---|---|---|
| Applications | ||
| A. TitleThesis/Practicum (two course units) | ||
Register for 2 course units of DATS 5970 Master's Thesis Research/Master’s Thesis or 2 course units of DATS 5990 Master's Indep Study/Master’s Independent Study. 2 | ||
| B. Bio medicine | ||
| Brain-Computer Interfaces | ||
| Networked Neuroscience | ||
| Advanced Methods and Health Applications in Machine Learning | ||
| Natural Language Processing for Health | ||
| Fundamentals of Computational Biology | ||
| Biomedical Image Analysis | ||
| Theoretical and Computational Neuroscience | ||
| C. Social/Network Science | ||
| Ethical Algorithm Design | ||
| Econometrics I: Fundamentals | ||
| Econometrics III: Advanced Techniques of Cross-Section Econometrics | ||
| Econometrics IV: Advanced Techniques of Time-Series Econometrics | ||
| Applied Probability Models in Marketing | ||
| D. Data-centric Programming | ||
| Software Systems | ||
| Database and Information Systems | ||
| Advanced Programming | ||
| Internet and Web Systems | ||
| Programming and Problem Solving | ||
| Software Engineering | ||
| Computer Systems Programming | ||
| E. Surveys and Statistical Methods | ||
| Data and Analysis for Marketing Decisions | ||
| Business Analytics | ||
| Sample Survey Methods | ||
| Observational Studies | ||
| Modern Regression for the Social, Behavioral and Biological Sciences | ||
| Accelerated Regression Analysis for Business | ||
| Forecasting Methods for Management | ||
| Predictive Analytics for Business | ||
| F. Data Analysis, Artificial Intelligence | ||
| Artificial Intelligence | ||
| Deep Learning for Data Science | ||
| Natural Language Processing | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Advanced Topics in Machine Learning | ||
| Theory of Machine Learning | ||
| Advanced Topics in Machine Perception | ||
| Graph Neural Networks | ||
| Learning in Robotics | ||
| Modern Data Mining | ||
| Principles of Deep Learning | ||
| G. Simulation Methods for Natural Science / Engineering | ||
| Molecular Modeling and Simulations | ||
| Computational Science of Energy and Chemical Transformations | ||
| Multiscale Modeling of Chemical and Biological Systems | ||
| Finite Element Analysis | ||
| Computational Mechanics | ||
| Atomic Modeling in Materials Science | ||
| H. Mathematical and Algorithmic Foundations | ||
| Advanced Linear Algebra | ||
| Analysis of Algorithms | ||
| Algorithms and Computation | ||
| Advanced Topics in Algorithms and Complexity | ||
| Numerical Methods and Modeling | ||
| Simulation Modeling and Analysis | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Data Mining: Learning from Massive Datasets | ||
| Modern Convex Optimization | ||
| Information Theory | ||
| Stochastic Models | ||
| Advanced Statistical Inference I | ||
| Bayesian Statistical Theory and Methods | ||
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Students must choose courses from 3 different buckets.
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Suggestions for projects will be provided to students. Students may choose from these suggested projects or may also come up with their own project/advisor ideas. Students will be mentored jointly by the Program Director and by an advisor in the area of the project, and must receive approval by Faculty Director.
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.
Penn’s online Master of Science in Engineering (MSE) in Data Science builds on the achievements of its on-campus counterpart, preparing students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications. No matter the discipline, fluency with data analysis methods is becoming essential in today’s world.
Flexible and accessible in its online format, MSE-DS Online is available for both the full-time and part-time student. Its curriculum dives deeply into topics such as artificial intelligence, big data systems, data science for health, deep learning, natural language processing, internet and web systems, machine learning, etc. Graduates in MSE-DS Online will be able to apply a background in scalable, robust computational and statistical methods in whatever field they choose to pursue.
For more information: https://online.seas.upenn.edu/degrees/mse-ds-online/
For students interested in learning more about the MSE in Data Science on campus program, click here.
Curriculum
The ten required course units are divided into four categories: Foundations, Core Courses, Technical Electives and Open Electives. (As long as the prerequisites for the courses are met, students can complete these courses in any sequence.)
| Code | Title | Course Units |
|---|---|---|
| Foundations | 3 | |
| Computer Systems Programming | ||
| Algorithms and Computation | ||
| Fundamentals of Linear Algebra and Optimization | ||
or EAS 5160 | Mathematical Foundations for Machine Learning I: Probability | |
| Core Course | 4 | |
| Big Data Analytics | ||
| Database and Information Systems | ||
| Machine Learning for Data Science | ||
| Statistics for Data Science | ||
| Technical Electives | 2 | |
| Artificial Intelligence | ||
| Natural Language Processing | ||
| GPU Computing for Machine Learning Systems | ||
| Computer Vision & Computational Photography | ||
| Principles of Deep Learning | ||
| Technology Ethics and the Legal Landscape | ||
| How to Use Data | ||
| Imaging Informatics | ||
| Medical Image Analysis | ||
| Data Science Capstone | ||
| Open Electives | 1 | |
| Any online EAS/CIS/ESE course or 1 | ||
| Digital Health 2 | ||
| Total Course Units | 10 | |
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See Penn Engineering Online course catalog
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HCIN 6022 should only be taken as an elective if students plan to apply for the Graduate Certificate in Health Care and Technology from the Perelman School of Medicine. All SEAS students entering the Graduate Certificate in Health Care and Technology must take this course as part of their degree studies. SEAS students taking this course for credit in an engineering degree must complete a final project focused on an area of computer science.
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.