520 Fundamentals of Data Science: Introduction to foundational concepts, technologies, and theories of data and data science. This includes methods of data acquisition, cleaning, analysis, and visualization. Taught in Python.

550 Introduction to Statistical Modeling: Introduction to foundational concepts, theories, and techniques of statistical analysis for data science. Students will begin with descriptive statistics and probability, random variables and probability, significance tests and confidence intervals, and advance through linear and multiple regression. Students will also conduct analyses in R. This course is approachable for students with little statistical background.

560 Data Science for Business: This course explores the various ways data and science can be applied to business contexts. Particular emphasis will be placed on analytics using data to make informed business decisions. Approachable for students who have taken DTSC-550 or have an understanding of basic statistics and beginner-level experience with R.

575 Principles of Python Programming: This course will teach students the introductory skills of programming, problem solving and algorithmic thinking in Python. Topics include variables, input/output, conditional statements/logic, Boolean expressions, flow control, loops and functions. Approachable for students who have taken DTSC-520 or have beginner-level experience with Python.

580 Data Manipulation: This course focuses on the loading, manipulating, processing, cleaning, aggregating, and grouping of data. Students will practice on real world data sets, learning how to manipulate data using Python and continue their study of intermediate and advanced topics from the NumPy and Pandas libraries. Students should have taken DTSC 520 and DTSC 575, or have previous Python for data analysis knowledge/experience.

600 Information Visualization: This course is designed to teach students the best practices in Data Visualization, the key trends in the industry, and how to become great storytellers with data. Students taking this class will learn the importance of using actionable dashboards that enable their organizations to make data-driven decisions. For this class students will use Tableau, one of the most used visual analytics platforms in the industry.

620 Cloud Foundations: This course will introduce students to the advantages and vocabulary of cloud computing. Students will gain exposure and experience with cloud-based core resources for compute, storage, database, and networking tasks. Students will explore best practices for cloud architecture, including operational excellence, security, shared responsibility, cost optimization, reliability, and scalability.

650 Data Analytics in R: This course places emphasis on the most common statistical techniques used in modern data science. The first half of the course covers data cleaning and data visualization with the Tidyverse. The second half of the course covers correlation, linear, multiple, and logistic regression, along with assumptions, diagnostics, and variable selection methods. Approachable for students who have taken DTSC-550 or have statistical analysis experience in R.

660 Data and Database Management with SQL: This course offers a comprehensive overview of data organization and management using relational database systems and the SQL programming language. The course introduces students to database systems and their applications, with a focus on designing and implementing database solutions based on user and data requirements.

670 Foundations of Machine Learning Models: Introduction to the machine learning landscape. Will address questions such as what is machine learning, why do we use machine learning, and what is machine learning appropriate and inappropriate for? The course will explore supervised and unsupervised learning, regression and classification models, decision trees and ensemble learning, along with other traditional machine learning algorithms. Taught in Python. Students should have taken DTSC 520, DTSC 575, and DTSC 580 or have previous Python for data analysis knowledge/experience.

675 Mathematics for Data Science: This course provides a comprehensive introduction to the mathematical foundations of data science. Students will explore topics in linear algebra and multivariate calculus, focusing on their applications in data science. The course aims to build the mathematical framework necessary for understanding various machine learning models and algorithms. Python programming will be used throughout the course to reinforce learning concepts. Prerequisite: DTSC-670 must be completed before taking this course. Previous experience in calculus 1 is necessary to be successful in this course.

690 Data Science Capstone: Ethical and Philosophical Issues in Data Science Students will explore contemporary ethical and philosophical issues in data science, analytics, and artificial intelligence. Students will engage with a wide range of interdisciplinary readings examining moral challenges and responsibilities inherent in the development and deployment of new data-driven technologies. Topics include societal and psychological impacts of AI, challenges of misinformation and algorithmic bias, the complexities of privacy and surveillance, and global implications of technological development. Prerequisites: Students must have completed 15 credits to register.

691 Data Science Capstone: Applied Data Science:Students will complete a capstone project challenging students to integrate and apply the knowledge and skills gained throughout their coursework. Students will conceive and execute a comprehensive project, from proposal through final presentation. The capstone project is a showcase of student capability to independently navigate complex, data-centric problems, and formulate viable, data-driven solutions. Prerequisites: Students must have completed 15 credits to register.

BUSA 505 The Business Environment: Organizations of every size utilize core business functions and concepts such as marketing, human resources, accounting, financial management, economics, information systems, and operations management. Managers must understand these functional areas and the relationships between them in order to competently lead their team within the organization. This foundational course in the MBA program provides this necessary context and sets the stage for students to delve deeper into these concepts throughout their program. 

BUSA 585 Financial Accounting: This course overviews the processes of financial reporting, summation, and analysis. Students will learn how to prepare and interpret financial statements, evaluate an organization’s financial health, and forecast future financial decisions. The emphasis of this course will be on the managerial insights of financial accounting rather than day-to-day accounting practices. Students will be equipped with the skills and knowledge to effectively and responsibly manage the financial aspects of their teams, departments, and organizations.

HMGT 526 Healthcare Finance and Economics: A critical review of the areas of finance and economics as they affect the U.S. Healthcare Industry. This course expands on core finance and economics MBA courses by focusing on the unique applications healthcare professionals need to successfully contribute to their organizations. These include revenue streams, third party payers, planning, pricing, performance measurement, supply, demand, elasticity, public policy, and consumer behavior. Additionally, students consider the ethical and legal ramifications of finance, economics and healthcare. Students are strongly recommended to successfully complete BUSA 585 prior to HMGT 526.