DTSC 520: Fundamentals of Data Science (3 credits): Introduction to foundational concepts, technologies, and theories of data and data science.  This includes methods in data acquisition, cleaning, and visualization.  Taught in Python using NumPy, Pandas, Matplotlib, and Git. Includes an introduction to Python, IPython, and Jupyter Notebooks.  

DTSC 550: Introduction to Statistical Modeling (3 credits): Introduction to foundational concepts, theories, and techniques of statistical analysis for data science.  Students will begin with descriptive statistics and probability, and advance through multiple and logistic regression.  Students will also conduct analyses in R.  This course is approachable for students with little statistical background and prepares them for DTSC 650: Data Analytics in R.

DTSC 560: Data Science for Business (3 credits):  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 an understanding of basic statistics and beginner-level experience with R.

DTSC 575: Principles of Python Programming (3 credits): 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 no experience with Python. 

DSTC 580: Data Manipulation (3 credits): 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 either taken DTSC520 or have previous Python for data analysis knowledge/experience.   

DTSC 600: Information Visualization (3 credits): 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. 

DTSC 650: Data Analytics in R (3 credits): Continuation of DTSC 550, with an emphasis on statistical techniques most used in modern data science.  Will explore in greater depth linear and logistic regression, and continue additional regression and classification techniques with a focus on application.  Analyses will be completed in R. 

DTSC 660: Data and Database Management with SQL (3 credits): 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. 

DTSC 670: Foundations of Machine Learning Models (3 credits): Introduction to machine learning landscape.  Will address questions such as what is machine learning?  Why do we use machine learning?  What is machine learning appropriate for?  What is it inappropriate for?  Will explore supervised and unsupervised learning, regression and classification models, and support vector machines. Taught in Python. 

DTSC 680: Applied Machine Learning (3 credits): Continuation of DTSC 670. Further exploration of modern machine learning applications.  Topics include neural networks and deep learning, including an emphasis on model selection and tuning. Taught in Python.  Prerequisite: DTSC 670: Foundations of Machine Learning.

DTSC 685: Natural Language Processing (3 credits):  This course will introduce the field of Natural Language Processing (NLP) 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.  Prerequisite: DTSC 580 Data Manipulation, DTSC 670: Foundations of Machine Learning Models.

DTSC 690: Data Science Capstone: Ethical and Philosophical Issues in Data Science (3 credits): Part one of the capstone in the Masters in Data Science.  Students will explore contemporary ethical and philosophical issues in data science and artificial intelligence.  Subjects include basic and advanced issues, ranging from social media privacy to implications of machine learning and artificial intelligence for religiousness.  Can be taken jointly with DTSC 691.  Prerequisite: DTSC 670: Foundations of Machine Learning Models. 

DTSC 691: Data Science Capstone: Applied Data Science (3 credits): Part two of the capstone in the Masters in Data Science.  Students will also complete a capstone project integrating their learning across courses.  Students will complete a project proposal, including their data source, acquisition, cleaning, analysis, and presentation intentions.  Can be taken jointly with DTSC 690.  Prerequisite: DTSC 670: Foundations of Machine Learning Models.