Introducing Data Science Topics to Non-Computing Majors

Xumin Liu, Erik Golen, Rajendra K. Raj

Abstract: Data science knowledge and skills have become indispensable to STEM and non-STEM disciplines alike. As a result, it has become crucial for students in non-computing majors to learn data science techniques, particularly in the context of their own disciplines. A majority of current university data science coursework, however, requires sufficient depth in programming and statistical skills related to managing, manipulating, and analyzing data, which reduces their usefulness for entry-level non-computing majors. This workshop presents a set of hands-on exercises to introduce data science to entry-level non-computing majors. The exercises cover the data science lifecycle, including data acquisition, preparation, model development and deployment, visualization, and storytelling. A freely-available web-based Data Science Learning Platform (DSLP) will be presented to show how to perform hands-on data science exercises with little or no coding background. The presenters will also share their experiences in using the DSLP tool in an entry-level data science course to non-computing majors at RIT. Both the tool and course materials will be shared with workshop participants. The typical workshop participant is a high school teacher or a college instructor interested in teaching data science at the introductory level. No prior programming or data science experience is needed, thus making the workshop materials usable by a wide audience. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This work was supported by the National Science Foundation under Award 2021287.