Fall 2026 Courses

MAT 616 ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE (Offered Spring 2027)

Prerequisites: Instructor Permission

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.

MAT 646 INTRODUCTION TO STATISTICS FOR DATA SCIENCE

Prerequisite: Instructor permission.

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. 

CIS 512 INTRODUCTION TO DATA SCIENCE AND ANALYTICS

Prerequisites: Graduate Standing

Introduction to data analysis in Excel, Tableau, and Python; execute queries to extract data from a relational database; Data Science Life Cycle; tools and techniques to perform all the phases of the data science life cycle; Introduction to Machine Learning concepts.

DSA 587 Introduction to AI in Investment

This course is an elective cross-disciplinary course. We first introduce basic concepts of financial markets, issues in the practice of investment, and the traditional approaches to address these issues, such as fundamental and technical analysis. Then, we add com-puting tools, including Python libraries, data APIs, and backtesting platforms. All of them prepare students to apply AI in investment decision process, with the emphasis on collecting data, wrangling it, analyzing it, creating informative signals, predicting using Ma-chine Learning and/or LLMs tools, evaluating and interpreting the results. Case studies will be discussed.

PSM 601 PROJECT MANAGEMENT FOR MATH AND SCIENCE PROFESSIONALS

Prerequisites:  Graduate standing

Current practices in project management as applied to math and science projects. Hands-on experience with the skills, tools, and techniques required in different phases of a project's life cycle, including project selection, project planning, project staffing and organization, task scheduling, project scope management, budgeting and progress reporting, risk management, quality management, project communications, and use of appropriate project management software tools. Techniques for communicating and motivating teams throughout the project life cycle. Emphasis on team building and practicing project management techniques through the use of science-based cases.

DSA 501 DATA ORIENTED COMPUTING AND ANALYTICS (Update: Focus on SQL)

Prerequisite: Instructor permission.

Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports.

HEA 730 DATA VISUALIZATION AND STORYTELLING (offered Spring 2027)

Prerequisite: Instructor permission.

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 688 EXPERIENTIAL LEARNING IN DATA SCIENCE AND ANALYTICS

Prerequisites: Graduate standing, instructor permission, and 3.0 minimum GPA

Internship and team project (a.k.a. professional labs). Internships acquaint students with specialized resources in industry. Professional labs allow students to apply skills to real-world challenges. Offered every semester, beginning spring 2024.