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.

Curriculum

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

Foundations (2 cu's)
CIT 590Programming Languages and Techniques1
or CIT 591 Introduction to Software Development
Select one of the following:
CIS 515Fundamentals of Linear Algebra and Optimization1
or MATH 513 Computational Linear Algebra
Core Requirements (3 cu's)
ESE 542Statistics for Data Science1
CIS 545Big Data Analytics1
Select one of the following:
CIS 519Applied Machine Learning1
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 below5
Total Course Units10

Technical Electives1

Applications
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. 2
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 711Forecasting Methods for Management1
STAT 722Predictive 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
 

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.