- Concentrations and course offerings may vary by campus and/or by program modality. Please consult with your advisor or admissions coach for the course availability each term at your campus or within your program modality.
- Certain options within the program may be required at certain campuses or for certain program modalities. Please consult with your advisor or admissions coach for requirements at your campus or for your program modality.
Complete all courses and requirements listed below unless otherwise indicated.
Students should refer to the course numbering table for graduate course leveling.
Core Requirements
A cumulative GPA of 3.000 or higher is required in the following core courses.
Code | Title | Hours |
---|---|---|
Complete 20 semester hours from the following: | ||
Data Management and Processing | ||
DS 5110 | Introduction to Data Management and Processing | 4 |
Algorithms | ||
Complete 4 semester hours from the following: | 4 | |
Algorithms | ||
Fundamentals of Computer Engineering | ||
Machine Learning and Data Mining | ||
DS 5220 | Supervised Machine Learning and Learning Theory | 4 |
DS 5230 | Unsupervised Machine Learning and Data Mining | 4 |
Presentation and Visualization | ||
DS 5500 | Data Science Capstone | 4 |
Electives
Code | Title | Hours |
---|---|---|
Complete 12 semester hours from the following: 1 | 12 | |
Khoury College of Computer Sciences | ||
Foundations of Artificial Intelligence | ||
Reinforcement Learning and Sequential Decision Making | ||
Database Management Systems | ||
Pattern Recognition and Computer Vision | ||
Computer/Human Interaction | ||
Web Development | ||
Natural Language Processing | ||
Information Retrieval | ||
Large-Scale Parallel Data Processing | ||
Empirical Research Methods | ||
Fundamentals of Cloud Computing | ||
Building Scalable Distributed Systems | ||
Advanced Machine Learning | ||
Deep Learning | ||
Special Topics in Artificial Intelligence | ||
Statistical Methods for Computer Science | ||
Information Visualization: Theory and Applications | ||
Special Topics in Database Management | ||
Special Topics in Data Science | ||
Topics in Data Science | ||
Thesis | ||
Project | ||
College of Engineering | ||
Time Series and Geospatial Data Sciences | ||
Statistical Inference: An Introduction for Engineers and Data Analysts | ||
Computer Vision | ||
High-Performance Computing | ||
Parallel Processing for Data Analytics | ||
Information Theory | ||
Advanced Computer Vision | ||
Advanced Machine Learning | ||
Data Management for Analytics | ||
Statistical Methods in Engineering | ||
College of Social Sciences and Humanities | ||
Applied Econometrics | ||
Dynamic Modeling for Environmental Decision Making | ||
Big Data for Cities | ||
Geographic Information Systems for Urban and Regional Policy | ||
Urban Theory and Science | ||
Advanced Spatial Analysis of Urban Systems | ||
College of Science | ||
Advanced Spatial Analysis | ||
Network Science 1 | ||
Statistical Physics | ||
Computational Physics | ||
Bouvé College of Health Sciences | ||
Introduction to Epidemiology | ||
Biostatistics in Public Health | ||
Social Epidemiology | ||
College of Arts, Media and Design | ||
Game Design and Analysis | ||
Data-Driven Player Modeling |
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
- 1
Students taking electives worth less than 4 semester hours (i.e., Bouvé courses) should enroll for an accompanying data science project course in the same semester to bring the cumulative semester hours to 4. In order to earn this additional credit, students are expected to work with faculty to design an additional project in line with the curricular aims of their chosen elective and the data science core learning outcomes.