Our curriculum prepares you to design tools that collect, evaluate and interpret data to inform critical decisions. Through rigorous, mathematically based coursework, learners master advanced concepts for a strong foundation for professional success and self-direction.
"In my final semester, I built a neural network - a predictive model for heart disease. I was able to go through a start to finish project. Going forward…it’s invaluable."
Accelerated python programming course for students who have not had a for-credit Python programming course before.
Essential Math for Applied Data Science & AI (COMP 3009)
Covers basics needed for data science and machine learning, covering basic linear algebra, basic calculus, and basic probability and statistics.
Python Software Development (COMP 3006)
Continuation of python programming focusing on object oriented design and core libraries for data and manipulation and computation.
Database Organization and Management I (COMP 3421)
Introduction to relational and nonrelational database management system design and operation.
Intro to Probability & Statistics for Data Science & AI (COMP 4441)
Applied class in inferential statistics that delves into parametric and nonparametric testing as well as linear models.
Machine Learning (COMP 4432)
Introduction to machine learning techniques as applied to data science and AI
Data Visualization (COMP 4433)
Theory, selection, and design of visualizations to support analytical problem-solving and technical communication with data
Deep Learning: Model Design & Application (COMP 4531)
Introduction to artificial neural network models and optimization techniques focusing on multi-layer architectures and CNNs
Elective Courses
Advanced Probability & Statistics for Data Science & AI (COMP 4442)
Advanced statistical techniques for classification, prediction, simulation, and dimensional reduction
Parallel & Distributed Computing for Data Science & AI (COMP 4334)
Applied parallel and distributed machine learning for big data analytics
Algorithms for Data Science & AI (COMP 4581)
Algorithmic design and data structures to solve computational problems and process large datasets
Data Science & AI Capstone (COMP 4449)
Design, develop, test, and present 'full-cycle' data science products or services
Machine Learning Operations (COMP 4450)
Introduction to model and data development, data engineering, and scalable model deployment using industry standard techniques such as containerization.
Deep Learning for Sequence Data (COMP 4456)
Application of deep learning models for natural language processing and time series data
Large Language Models (LLMs) for Data Science & AI (COMP 4451)
Introduction to using LLMs as a tool for data science problems
Data Science & AI Independent Study (COMP 4991)
Study of a particular topic supervised by a faculty member
Internship (COMP 3904)
Receive credit toward graduation for participating in a paid data science and/or AI internship.
Go to the graduate admission application to submit your information. For information on admission requirements, visit the graduate academic programs page and locate your program of interest.
