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.

Programming with Data
DS 5110Essentials of Data Science4
Algorithms
CS 5800Algorithms4
or EECE 7205 Fundamentals of Computer Engineering
Machine Learning
CS 6140Machine Learning4
or EECE 5644 Introduction to Machine Learning and Pattern Recognition
Interdisciplinary Capstone
DS 5500Data Science Capstone4

Data Science Concentration Options

Complete one of the following concentrations:

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required


Computer Science Concentration—Khoury College of Computer Sciences

Complete 16 semester hours from the following: 116
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

Foundational Courses
Complete 4 semester hours from the following: 14
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.