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. Prerequisites: DTSC-670 must be completed before taking this course. Previous experience in calculus 1 is necessary to be successful in this course.

680 Applied Machine Learning: Continuation of DTSC 670. This course will further explore modern machine learning applications such as deep learning methods. Special attention will be given to image classification and object detection. Students will also focus on different dimensionality reduction techniques with emphasis on using principal component analysis. Additionally, students will learn to operationalize machine learning models using Flask.

685 Natural Language Processing: This course will introduce the field of Natural Language Processing and its related algorithms and ideas. Students will gain experience writing NLP algorithmic code in python, as well as working through text-based machine learning problems.

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.

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