Complete all courses and requirements listed below unless otherwise indicated.
Students should refer to the course numbering table for graduate course leveling.
Data Science Core
A cumulative GPA of 3.000 or higher is required in the following core courses.
| Code | Title | Hours |
|---|---|---|
| Programming with Data | ||
| DS 5110 | Essentials of Data Science | 4 |
| Algorithms | ||
| CS 5800 | Algorithms | 4 |
| or EECE 7205 | Fundamentals of Computer Engineering | |
| Machine Learning | ||
| CS 6140 | Machine Learning | 4 |
| or EECE 5644 | Introduction to Machine Learning and Pattern Recognition | |
| Interdisciplinary Capstone | ||
| DS 5500 | Data Science Capstone | 4 |
Data Science Concentration Options
Complete one of the following concentrations:
- Computer Science—Khoury College of Computer Sciences
- Engineering Theory and Modeling—College of Engineering
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
Computer Science Concentration—Khoury College of Computer Sciences
| Code | Title | Hours |
|---|---|---|
| Complete 16 semester hours from the following: 1 | 16 | |
| 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 | ||
| Data Mining Techniques | ||
| 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 | ||
| Thesis | ||
| Master’s Project | ||
| Project | ||
Engineering Theory and Modeling Concentration—College of Engineering
| Code | Title | Hours |
|---|---|---|
| Foundational Courses | ||
| Complete 4 semester hours from the following: 1 | 4 | |
| Project | ||
| Combinatorial Optimization | ||
| Statistical Inference: An Introduction for Engineers and Data Analysts | ||
| Applied Probability and Stochastic Processes | ||
| Numerical Optimization Methods | ||
| Information Theory | ||
| Probabilistic System Modeling and Analysis | ||
| Foundations for Data Analytics Engineering | ||
| Data Mining in Engineering | ||
| Statistical Methods in Engineering | ||
| Translational and Advanced Courses | ||
| Complete the remaining 12 semester hours from the following: | 12 | |
| Modeling and Inference in Bioengineering | ||
| Computational Methods in Systems Bioengineering | ||
| Mathematical Methods in Bioengineering | ||
| Computational Modeling in Chemical Engineering | ||
| Numerical Strategies and Data Analytics for Chemical Sciences | ||
| Time Series and Geospatial Data Sciences | ||
| Data-Driven Decision Support for Civil and Environmental Engineering | ||
| Combinatorial Optimization | ||
| Statistical Inference: An Introduction for Engineers and Data Analysts | ||
| Reinforcement Learning and Decision Making Under Uncertainty | ||
| Image Processing and Pattern Recognition | ||
| Computer Vision | ||
| High-Performance Computing | ||
| Data Visualization | ||
| Parallel Processing for Data Analytics | ||
| Applied Probability and Stochastic Processes | ||
| Introduction to Distributed Intelligence | ||
| Riemannian Optimization | ||
| Numerical Optimization Methods | ||
| Information Theory | ||
| Big Data and Sparsity in Control, Machine Learning, and Optimization | ||
| Probabilistic System Modeling and Analysis | ||
| Advanced Computer Vision | ||
| Advanced Machine Learning | ||
| Master’s Project | ||
| Computational Modeling in Industrial Engineering | ||
| Structured Data Analytics for Industrial Engineering | ||
| Biosensor and Human Behavior Measurement | ||
| Data Mining for Engineering Applications | ||
| Foundations for Data Analytics Engineering | ||
| Computation and Visualization for Analytics | ||
| Data Management for Analytics | ||
| Data Warehousing and Integration | ||
| Intelligent Manufacturing | ||
| Data Mining in Engineering | ||
| Statistical Methods in Engineering | ||
| Applied Reinforcement Learning in Engineering | ||
| Statistical Learning for Engineering | ||
| Applied Natural Language Processing in Engineering | ||
| Neural Networks and Deep Learning | ||
- 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 hour, 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.