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.


10 course units are required for the Data Science degree.1 

CIT 590Programming Languages and Techniques.1
or CIT 591 Introduction to Software Development
Select one of the following: 21
Introduction to Probability and Statistics
Advanced Probability
Core Requirements
Select one of the following:1
Mathematical Statistics
Fundamentals of Linear Algebra and Optimization
Theory of Machine Learning
CIS 545Big Data Analytics1
Select one of the following:1
Introduction to Machine Learning
Machine Learning
Modern Data Mining
Technical & Depth Area Electives
Select 5 electives5
Total Course Units10

Technical & Depth Area Electives1

A. TitleThesis/Practicum (two course units)
Register for 2 course units of DATS 597 Master's Thesis Research/Master’s Thesis or 2 course units of DATS 599 Master's Indep Study/Master’s Independent Study. 1
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:
Micro and Nano Fluidics
Nanoscale Systems Biology
Fundamental Techniques of Imaging I
Biomedical Image Analysis
Materials Science and Engineering:
Phase Transformations
Elasticity and Micromechanics of Materials
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

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.