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 – a depth area – of choice.
The depth area 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 | ||
| CIT 590 | Programming Languages and Techniques. | 1 |
| or CIT 591 | Introduction to Software Development | |
| Select one of the following: 2 | 1 | |
| Introduction to Probability and Statistics | ||
or STAT 510 | Probability | |
or MATH 546 | Advanced Probability | |
| Core Requirements | ||
| Select one of the following: | 1 | |
| Mathematical Statistics | ||
| Fundamentals of Linear Algebra and Optimization | ||
| Theory of Machine Learning | ||
| CIS 545 | Big Data Analytics | 1 |
| Select one of the following: | 1 | |
| Introduction to Machine Learning | ||
or CIS 520 | Machine Learning | |
or STAT 571 | Modern Data Mining | |
| Technical & Depth Area Electives | ||
| Select 5 electives | 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 & Depth Area electives. (As long as the prerequisites for the courses are met, students can complete these courses in any sequence) |
| 2 | In lieu of these courses, students may take Technical Electives and are encouraged (but not required) to take a course from Bucket C in lieu of Probability, and a course from Bucket B in lieu of PL. |
| 3 | Students must choose courses from 3 different buckets, one bucket of which can be a 2 semester sequence of thesis/practicum. Two of the courses must represent a depth sequence, which could be the thesis/practicum or (for bucket options B-J) two courses, one of which builds on the other (e.g. is a prerequisite). |
Technical & Depth Area Electives1
| Code | Title | Course Units |
|---|---|---|
| Applications | ||
| A. TitleThesis/Practicum (two course units) | ||
| B. Bio medicine | ||
| Brain-Computer Interfaces | ||
| Network Neuroscience | ||
| Mathematical Computation Methods for Modeling Biological Systems | ||
| C. Social/Network Science | ||
| Econometrics I: Fundamentals | ||
| Econometrics II: Methods & Models | ||
| Econometrics III: Advanced Techniques of Cross-Section Econometrics | ||
| Econometrics IV: Advanced Techniques of Time-Series Econometrics | ||
| Applied Probability Models in Marketing | ||
| D. Natural Science/Engineering 3 | ||
| Chemical Engineering: | ||
| Advanced Chemical Kinetics and Reactor Design | ||
| Transport Processes II (Nanoscale Transport) | ||
| Interfacial Phenomena. | ||
| Mechanical Engineering: | ||
| Aerodynamics | ||
| Nanotribology | ||
| Micro and Nano Fluidics | ||
| Bioengineering: | ||
| Nanoscale Systems Biology | ||
| Fundamental Techniques of Imaging I | ||
| Biomedical Image Analysis | ||
| Materials Science and Engineering: | ||
| Nanotribology | ||
| Phase Transformations | ||
| Elasticity and Micromechanics of Materials | ||
| Methods | ||
| E. Data-centric Programming | ||
| Software Systems | ||
| Software Engineering | ||
| Computer Systems Programming | ||
| Advanced Programming | ||
| Internet and Web Systems | ||
| Programming and Problem Solving | ||
| F. Data Collection, Representation, Management and Retrieval | ||
| Database and Information Systems | ||
| Sample Survey Methods | ||
| Observational Studies | ||
| G. Data Analysis, Artificial Intelligence | ||
| Computational Linguistics | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Advanced Topics in Machine Perception | ||
| Theory of Machine Learning | ||
| Data Mining: Learning from Massive Datasets | ||
| Modern Data Mining | ||
| Analysis of Algorithms | ||
| Artificial Intelligence | ||
| Advanced Topics in Algorithms and Complexity | ||
| Special Topics | ||
| Algorithms and Computation | ||
| Learning in Robotics | ||
| Modern Regression for the Social, Behavioral and Biological Sciences | ||
| H. Simulation Methods for Natural Science/Engineering | ||
| Multiscale Modeling of Chemical Systems | ||
| Molecular Modeling and Simulations | ||
| Computational Science of Energy and Chemical Transformations | ||
| Finite Element Analysis | ||
| Computational Mechanics | ||
| Atomic Modeling in Materials Science | ||
| I. Modelling | ||
| Topics In Computational Science and Engineering | ||
| Feedback Control Design and Analysis | ||
| J. Statistics, Mathematical Foundations | ||
| Complex Analysis | ||
| Fundamentals of Linear Algebra and Optimization | ||
| Numerical Methods and Modeling | ||
| Intro to Linear, Nonlinear and Integer Optimization | ||
| Modern Convex Optimization | ||
| Information Theory | ||
| Stochastic Processes | ||
| Accelerated Regression Analysis for Business | ||
| 1 | Students must choose courses from 3 different buckets, one bucket of which can be a 2 semester sequence of thesis/practicum. Two of the courses must represent a depth sequence, which could be the thesis/practicum or (for bucket options B-J) two courses, one of which builds on the other (e.g. is a prerequisite). |
| 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. |
| 3 | Generally, any course in which the primary focus is a physical/chemical/biological/mechanical application area that may be studied computationally is allowed. |
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.