Spring 2026 Courses

DSA 601 : MACHINE LEARNING MODELS IN PYTHON

Prerequisites: MAT 126, MAT 311, CIS 512 OR INSTRUCTOR PERMISSION

Course Description: Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naive Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.

DSA 610 : DATABASE & INFO CYCLE

Prerequisites: None.

Course Description: Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice. Offered occasionally, beginning spring 2020.

DSA 650 : DATA STRATEGY & GOVERNANCE

Prerequisites: None.

Format: HY; ONLINE ACTIVITY MIXED WITH CLASSROOM MEETINGS, REPLACING AT LEAST 20 PERCENT, BUT NOT ALL, REQUIRED ON-SITE MEETINGS.

Course Description: Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.

DSA 688 : EXPERIENTIAL LEARNING IN DSA

Prerequisites: Graduate status. THIS SECTION REQUIRES INSTRUCTOR PERMISSION

Course Description: This course will have 3 components; internship, professional lab, and seminar. The internship will acquaint the student with the specialized resources of various external organizations and will assist the student in understanding the nature of employment activities in offices/agencies that employ data scientists. The professional lab will provide the student with an opportunity to apply the skills and knowledge gained in the classroom to actual problems and tasks while still in an academic environment.

MAT 646 : INTRODUCTION TO STATISTICS FOR DATA SCIENCE

Prerequisite: Instructor permission.

Course Description: Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance.

MAT 616 : ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE

Prerequisites: Instructor permission

Course Description: Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods.

HEA 730 : DATA VISUALIZATION AND STORYTELLING

Prerequisites: None.

Course Description: This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.

DSA 587 / DSA 502 : DATA SCIENCE WITH AI

Prerequisites: None.

Course Description: This course explores the practical application of rapidly advancing Generative AI tools within the broader field of Data Science. It introduces core concepts such as Large Language Models, Transformer architectures, Prompt Engineering, and Retrieval-Augmented Generation while comparing cloud-based AI platforms with local open-source models. Students gain experience using Python, HTML/CSS, and AI-driven development tools through real-world case studies across industries. In the later part of the course, students learn to integrate Generative AI into data science workflows, developing proficiency in building AI-assisted applications, analyzing data using modern ML techniques, and applying responsible AI practices to solve practical data challenges.

DSA 301 / MAT 366 : DATA SCIENCE AND ANALYTICS WITH SPREADSHEETS, DBS AND PYTHON (3 Credits)

Prerequisites: MAT 241 or instructor permission

Course Description: Introduction to the fundamental tools and techniques used to collect, clean, analyze, and present data across academic and professional domains. Topics include data scraping from the internet, data management using spreadsheets and databases, and data analysis, visualization, and reporting using Python. Emphasis is placed on practical, hands-on experience with real-world datasets and developing core analytical skills applicable across disciplines. Offered occasionally.