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/
Curriculum
10 course units are required for the Data Science degree.1
| Code | Title | Course Units |
|---|---|---|
| Foundations (2 cu's) | ||
| CIT 590 | Programming Languages and Techniques | 1 |
| or CIT 591 | Introduction to Software Development | |
| Select one of the following: | ||
| CIS 515 | Fundamentals of Linear Algebra and Optimization | 1 |
| or MATH 513 | Computational Linear Algebra | |
| Core Requirements (3 cu's) | ||
| ESE 542 | Statistics for Data Science | 1 |
| CIS 545 | Big Data Analytics | 1 |
| Select one of the following: | ||
| CIS 519 | Applied Machine Learning | 1 |
| or CIS 520 | Machine Learning | |
| or STAT 571 | Modern Data Mining | |
| or ENM 531 | Data-driven Modeling and Probabilistic Scientific Computing | |
| or ESE 545 | 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 | |
| 1 | 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) | ||
| B. Bio medicine | ||
| Brain-Computer Interfaces | ||
| Networked Neuroscience | ||
| Mathematical Computation Methods for Modeling Biological Systems | ||
| Fundamentals of Computational Biology | ||
| Biomedical Image Analysis | ||
| Theoretical and Computational Neuroscience | ||
| C. Social/Network Science | ||
| 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 | ||
STAT 910 | ||
| Sample Survey Methods | ||
| Observational Studies | ||
| Modern Regression for the Social, Behavioral and Biological Sciences | ||
| Accelerated Regression Analysis for Business | ||
| STAT 711 | Forecasting Methods for Management | 1 |
| STAT 722 | Predictive Analytics for Business (formerly STAT 622) | 1 |
| F. Data Analysis, Artificial Intelligence | ||
| Artificial Intelligence | ||
| Deep Learning for Data Science | ||
| Computational Linguistics | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Advanced Topics in Machine Learning | ||
| Advanced Topics in Machine Perception | ||
| Learning in Robotics | ||
| Modern Data Mining | ||
| Priniples of Deep Learning | ||
| G. Simulation Methods for Natural Science / Engineering | ||
| Molecular Modeling and Simulations | ||
| Computational Science of Energy and Chemical Transformations | ||
| Finite Element Analysis | ||
| Computational Mechanics | ||
| Atomic Modeling in Materials Science | ||
| Multiscale Modeling of Chemical Systems | ||
| Mathematical Computation Methods for Modeling Biological Systems | ||
| H. Mathematical and Algorithmic Foundations | ||
| Advanced Linear Algebra | ||
| Analysis of Algorithms | ||
| Theory of Machine Learning | ||
| Advanced Topics in Algorithms and Complexity | ||
| Numerical Methods and Modeling | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Intro to Linear, Nonlinear and Integer Optimization | ||
| Data Mining: Learning from Massive Datasets | ||
| Simulation Modeling and Analysis | ||
| Modern Convex Optimization | ||
| Information Theory | ||
| Stochastic Processes | ||
| Mathematical Statistics | ||
| Bayesian Statistical Theory and Methods | ||
| Algorithms and Computation | ||
| 1 | Students must choose courses from 3 different buckets. |
| 2 | 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 2020 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.