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 5900Programming Languages and Techniques1
or CIT 5910 Introduction to Software Development
Select one of the following:
CIS 5150Fundamentals of Linear Algebra and Optimization1
or MATH 5130 Computational Linear Algebra
Core Requirements (3 cu's)
ESE 5420Statistics for Data Science1
CIS 5450Big Data Analytics1
Select one of the following:
CIS 5190Applied Machine Learning1
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 below5
Total Course Units10
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

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
AI II: Introduction to Machine Learning and Health Language Processing
AI III: Advanced Methods and Health Applications in Machine Learning
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
Computational Linguistics
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
Priniples 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
Intro to Linear, Nonlinear and Integer Optimization
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
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 2022 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 students interested in learning more about the MSE in Data Science on campus program, click here.

Curriculum

10 course units are required for the MSE-DS Online degree. The ten course units are divided into three categories: 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.)

Core Courses4
Big Data Analytics
Database and Information Systems
Statistics for Data Science
Fundamentals of Linear Algebra and Optimization (*Note: If students have taken a similar course as part of another degree program, this course may be waived. In this case, it must be substituted with a technical elective.)
Technical Electives4
Artificial Intelligence
Deep Learning for Data Science
Computational Linguistics
Data Mining: Learning from Massive Datasets
Computer and Network Security
Internet and Web Systems
Computer Vision & Computational Photography
Blockchains and Cryptography
Open Electives2
DATO 5990 Practicum
Online CIS elective course
Note: Students may take CIT 5950 Computer Systems Programming and/or CIT 5960 Algorithms and Computation as one of the two Open Electives.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2022 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.