Data Science (DAT)
For more detailed course information, including contact hours and learning outcomes, review the relevant Master Course Syllabus.
College readiness test scores may be used to meet some prerequisite requirements. Learn more about college readiness, or talk to your advisor to see if test scores can apply.
If you took classes within the Colorado Community College System before Summer 2022, your transcript may list 3-digit course numbers instead of the current 4-digit format. To find the equivalent updated course numbers, refer to the Course Crosswalk.
DAT 1001 Introduction to Data Science 3 Credits
Provides a foundational overview of data science and develops the knowledge required to make data-driven decisions to address real-world problems. The course introduces how to collect data from different sources, use of statistics to draw conclusions about a given data set, use of technology to visualize data and some of the challenges associated with storing, manipulating, analyzing and securing data. Computational tools are used as a component of the course.
DAT 1002 Data Ethics Survey 3 Credits
Explores the ethical ramifications and implications surrounding the collection, analysis, and use of data. The course introduces the basics of ethical thinking in science, the history of ethical dilemmas in scientific work, and the modern challenges and relevant policies related to ethics in data science. Perspectives from both the consumer and the data scientist are considered when exploring themes such as privacy, equity, and Artificial Intelligence (AI).
DAT 2001 Calculus Based Statistics and Modeling 3 Credits
Introduces probability and statistics with an emphasis on computation, large data sets, and applications for engineering and data science careers. This course covers descriptive statistics, inferential methods, basic probability, predictive modeling, risk assessment, and methods of regression.
DAT 2002 Visualizing Data 3 Credits
Focuses on the analysis and design of visual representations of statistical information. The analysis and evaluation of existing graphics are combined with principles from disciplines such as statistics, computer science, and graphic design to define the criteria for a quality visualization. Various software tools are used to develop static and interactive visualizations to identify patterns, convey messages, make decisions, and tell stories with data.
DAT 2025 Introduction to Machine Learning 3 Credits
Provides the foundation for students to explore, practice, and apply mathematical concepts for pattern recognition, neural networks, and machine learning. The course covers algorithmic and mathematical methods which are required for machine learning techniques, as well as the theoretical relationships between these algorithms. Coding may be required as the course provides practice with translating the above mathematical concepts into computer programs.