Data Science and Artificial Intelligence, MSE

Penn’s Master of Science in Engineering (MSE) in Data Science & Artificial Intelligence (DATS & AI) prepares students for a wide range of data-driven and AI-powered careers, whether in technology and engineering, consulting, healthcare, science, policy-making, or understanding patterns in literature, art or communications. Building on a shared foundation in statistics, optimization, machine learning, and algorithms, students can tailor their studies through two concentrations—Data Science, which includes coursework on data management and decision making, and Artificial Intelligence, which includes coursework on building systems that can perform tasks that require human intelligence. The DATS & AI Program can typically be completed in one-and-a-half to two years.

The degree also allows students to apply what they have learned to a number of different application areas through a thesis or practicum. Potential areas of application can be explored through the many centers and institutes across campus – including trustworthy AI (the ASSET center), 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 more information: https://dats.seas.upenn.edu/program/

For students interested in learning more about MSE-DS Online, click here.
For students interested in learning more about MSE-AI Online, click here .

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Curriculum

10 course units are required for the Data Science & Artificial Intelligence degree.
The ten required course units are divided into three categories: Core Courses, Concentrations, and Electives. (As long as the prerequisites for the courses are met, students can complete these courses in any sequence.)

Course List
Code	Title	Course Units
Core Requirements (4 cu's)
Linear Algebra or Convex Optimization
CIS 5150	Fundamentals of Linear Algebra and Optimization	1
or MATH 5130	Computational Linear Algebra
or MATH 5140	Advanced Linear Algebra
or ESE 6050	Modern Convex Optimization
or STAT 5810	Convex Optimization for Statistics and Data Science
Statistics
ESE 5420	Statistics for Data Science	1
or STAT 5110	Statistical Inference
or STAT 5120	Mathematical Statistics
or STAT 5350	Forecasting Methods for Management
or STAT 5420	Bayesian Methods and Computation
Machine Learning
CIS 5190	Applied Machine Learning	1
or CIS 5200	Machine Learning
or ESE 5460	Principles of Deep Learning
Algorithms
CIS 5030	Algorithms for Big Data	1
or CIS 5020	Analysis of Algorithms
or CIS 6770	Advanced Topics in Algorithms and Complexity
Concentration (choose one)		6
Students are required to select one of the following tracks.
Data Science Concentration
CIS 5450	Big Data Analytics
CIS 5500	Database and Information Systems
or STAT 5710	Modern Data Mining
4 CUs of Electives (Choose courses from any of the elective buckets)
AI Concentration
CIS 5210	Artificial Intelligence
CIS 5810	Computer Vision & Computational Photography
or CIS 5300	Natural Language Processing
or CIS 6800	Advanced Topics in Machine Perception
or CIS 6300	Advanced Topics in Natural Language Processing
4 CU's of Electives (Two of which should come from the ML/multimodal AI bucket)
Total Course Units		10

Elective Buckets
Machine Learning, Multi-modal AI and Data Analysis

Course List
Code	Title	Course Units
CIS 5210	Artificial Intelligence
CIS 5220	Deep Learning for Data Science
CIS 5270	Trustworthy Machine Learning
CIS 5300	Natural Language Processing
CIS 5450	Big Data Analytics
CIS 5800	Machine Perception
CIS 5810	Computer Vision & Computational Photography
CIS 6200	Advanced Topics in Machine Learning
CIS 6250	Theory of Machine Learning
CIS 6300	Advanced Topics in Natural Language Processing
CIS 6800	Advanced Topics in Machine Perception
ESE 5140	Graph Neural Networks
ESE 5380	Machine Learning for Time-Series Data
ESE 5410	Machine Learning for Data Science
ESE 5460	Principles of Deep Learning
ESE 6180	Learning for Dynamics and Control
ESE 6450	Deep Generative Models
ESE 6500	Learning in Robotics
MSE 5760	Machine Learning and Its Applications in Materials Science
STAT 5710	Modern Data Mining

AI and Data Science for Discovery

Course List
Code	Title	Course Units
BE 5040	Biological Data Science II: Data Mining Principles for Epigenomics
BE 5060	Introduction to Neuroengineering
BE 5210	Brain-Computer Interfaces
BE 5660	Networked Neuroscience
BMIN 5030	Data Science for Biomedical Informatics
BMIN 5200	Foundations of Artificial Intelligence in Health
BMIN 5210	Advanced Methods and Health Applications in Machine Learning
BMIN 5220	Natural Language Processing for Health
BMIN 5490	Exploring Data Science Methods with Health Care Data
CIS 5350	Introduction to Bioinformatics
CIS 5360	Fundamentals of Computational Biology
CIS 5370	Biomedical Image Analysis
PHYS 5585	Theoretical and Computational Neuroscience

Optimization, Systems and Control

Course List
Code	Title	Course Units
ESE 5000	Linear Systems Theory
ESE 5050	Feedback Control Design and Analysis
ESE 5060	Introduction to Optimization Theory
ESE 6050	Modern Convex Optimization
ESE 6060	Combinatorial Optimization
ESE 6180	Learning for Dynamics and Control
ESE 6190	Model Predictive Control

Social and Network Science

Course List
Code	Title	Course Units
CIS 5230	Ethical Algorithm Design
ECON 7300	Econometrics I: Fundamentals
ECON 7310	Econometrics II: Methods & Models
ECON 8310	Econometrics III: Advanced Techniques of Cross-Section Econometrics
ECON 8320	Econometrics IV: Advanced Techniques of Time-Series Econometrics
MKTG 7760	Applied Probability Models in Marketing

Surveys and Statistical Methods

Course List
Code	Title	Course Units
MKTG 7120	Data and Analysis for Marketing Decisions
OIDD 6120	Business Analytics
STAT 5350	Forecasting Methods for Management
STAT 6210	Accelerated Regression Analysis for Business
STAT 7220	Predictive Analytics for Business
STAT 9200	Sample Survey Methods
STAT 9210	Observational Studies
STAT 9270	Bayesian Statistical Theory and Methods
STAT 9740	Modern Regression for the Social, Behavioral and Biological Sciences

Data-Centric Programming

Course List
Code	Title	Course Units
CIS 5050	Software Systems
CIS 5500	Database and Information Systems
CIS 5520	Advanced Programming
CIS 5550	Internet and Web Systems
CIS 5590	Programming and Problem Solving
CIS 5650	GPU Programming and Architecture
CIS 5690	GPU Computing for Machine Learning Systems
CIS 5730	Software Engineering
CIS 6500	Advanced Topics in Databases
CIT 5950	Computer Systems Programming
ESE 5390	Hardware/Software Co-Design for Machine Learning

Robotics

Course List
Code	Title	Course Units
ESE 6150	RoboRacer Autonomous Racing Cars
ESE 6500	Learning in Robotics
MEAM 5200	Introduction to Robotics
MEAM 6200	Advanced Robotics

Simulation

Course List
Code	Title	Course Units
CBE 5250	Molecular Modeling and Simulations
CBE 5440	Computational Science of Energy and Chemical Transformations
CBE 5590	Multiscale Modeling of Chemical and Biological Systems
MEAM 5270	Finite Element Analysis
MEAM 5610	Thermodynamics: Foundations, Energy, Materials
MEAM 6460	Computational Mechanics

Mathematical and Algorithmic Foundations

Course List
Code	Title	Course Units
AMCS 5141	Advanced Linear Algebra
CIS 5020	Analysis of Algorithms
CIS 5030	Algorithms for Big Data
CIS 6250	Theory of Machine Learning
CIS 6770	Advanced Topics in Algorithms and Complexity
CIT 5960	Algorithms and Computation
ENM 5020	Numerical Methods and Modeling
ENM 5310	Data-driven Modeling and Probabilistic Scientific Computing
ESE 5030	Simulation Modeling and Analysis
ESE 5060	Introduction to Optimization Theory
ESE 5450	Data Mining: Learning from Massive Datasets
ESE 6050	Modern Convex Optimization
ESE 6740	Information Theory
OIDD 9300	Stochastic Models
STAT 5150	Advanced Statistical Inference I
STAT 9270	Bayesian Statistical Theory and Methods

Other Electives

Course List
Code	Title	Course Units
CIS 5120	Introduction to Human Computer Interaction
CIS 7000	Special Topics ^{Only relevant data science / AI topics upon approval}

Thesis / Practicum

Course List
Code	Title	Course Units
DATS 5990	Master's Independent Study (1 or 2 cu's of Practicum Total (1 cu per semester)) ^{DATS Practicum and Thesis courses are not mandatory}
DATS 9990	Master's Thesis (2 cu's total needed (consecutive semesters)) ^{DATS Practicum and Thesis courses are not mandatory}