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In this paper we describe an NSF-IUSE funded project, which seeks to expose a broad group of undergraduate students to data science methods via already existing introductory undergraduate STEM classes (as opposed to through the creation of entirely new classes). A key component is a novel process, described here, to develop data science modules that naturally fit into such existing STEM classes. This novel process takes most of the development burden off the instructor's shoulders and instead employs collaborative teams of undergraduate and graduate students as well as the PIs in consultation with the instructor. The outcomes of this process are data science modules ready to be infused throughout the undergraduate STEM curriculum. We describe best practices and lessons learned about the module development and deployment process as well as several of the modules and their fit into the curriculum. In this paper we furthermore describe data science experiential learning opportunities for students at more advanced stages of their education. We finish by outlining the methods by which we will be evaluating the effectiveness of both the modules as well as the experiential learning opportunities in exposing students to data science methods.
Bonfert-Taylor, P., & Ray, L., & Pauls, S., & Loeb, L., & Sankey, L., & Busch, J., & Hickey, T. (2022, August), Infusing Data Science into the Undergraduate STEM Curriculum Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41919
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