Introduction to the field of data science, including both theoretical and applied components. Students till explore the origins of the field, including links to computer science, statistics, and mathematics. Students will use Python and associated data manipulation and visualization libraries to explore and analyze varied data sets.

This course is designed to teach students best practices in data visualization, key trends in the industry, and how to become better storytellers with data. Students will learn the imporance of using actionable dashboards that enable their organizations to make data-driven decisions.

This course will introduce statistical program R and build upon prior statistics knowledge. Students will both complete hand calculations and execute them in R.

This course considers the ways data can be organized, cleaned and managed within and between disparate data sets. More formal algorithmic techniques are emphasized with the end of prediction and analysis in mind.

In this course, students will use Python and its libraries to obtain, store, and clean data. Topics include data cleaning, data preparation, data joining and combining, and general data manipulation. This course assumes prior knowledge of Python, NumPy, and Pandas.

This course serves as a capstone for the Data Science Major. The student will apply the techniques learned to actual data sets in their chosen cognate area.

This course will introduce students to various ethical issues related to computing technology and the internet. Free/open source software, cybersecurity, privacy, monopoly power and artificial intelligence will be considered within a Christian framework.

This course serves as a capstone for the Data Science Major. The student will apply the techniques learned to actual data sets in their chosen cognate area.

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.

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.

Businesses have come to increasingly rely on data in all aspects of operation. This course explores the various ways data science skills can be applied to business scenarios. Topics include how to identify business decision problems and formulate research questions, how to use analytical techniques in spreadsheets and R to address these issues, and how these tools can inform decision making.

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.

Students will use Python to obtain, store, and clean data. Topics include connecting to databases, web scraping, time series data, and general data cleaning and preparation. This course assumes prior knowledge of Python, NumPy, and Pandas.

A thorough investigation of data visualization, emphasizing application. Draws upon insight from the fields of sensation and perception to understand basic principles involved in data visualization. Taught in Qlik and Tableau.

This course is a continuation of DTSC 550, with an emphasis on statistical techniques most used in modern data science. Students will explore in greater depth linear and logistic regression, and continue to additional regression and classification techniques with a focus on application. Analyses will be completed in R.

This course considers the ways data can be organized, cleaned and managed within and between disparate data sets. It also covers database design and the use of databases in data science applications with an emphasis on SQL. Additional topics include version control and Git.

Introduction to machine learning landscape. This course 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, such as k-nearest neighbors, support vector machines, decision trees, and principal component analysis. Taught in Python.

A 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.

Part one of the capsone 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.

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.

Students who have not successfully completed their DTSC 691 Applied Data Science coursework by the end of DTSC 691 must register for DTSC 692 until the project is completed. Gradings is pass/no credit. These credits do not accure although students are billed for three credits.