1C: Different perspectives on course topics in Intro data science…Including yours! (Room 108)


Michael Posner (Villanova University), April Kerby-Helm (Winona State University)


Abstract

"What is Data Science?" Despite no one having answered this perennial question (in a way that is broadly accepted), we are expected to set curricula in our classes. This session will explore what is being taught in data science classes from three perspectives. First, exploring what session participants teach in data science classes, including why and/or who chose it. Second, what are others teaching, through a review of a data science topics survey given to instructors from various disciplines, including syllabi from their data science courses. Lastly, through a review and discussion of professional organization recommendations for a data science course. Each perspective will be followed by small group discussions in order to improve our understanding of curriculum choices made by statisticians, computer scientists, and others who teach the variety of introductory data science courses that exist. The session’s goal is to explore and discuss various models for teaching data science and explore pros and cons of each one with the long-term goal of improving teaching undergraduate data science and increasing cross-disciplinary discussions and collaborations. (If you currently teach an undergraduate introductory data science course, please tell us what you teach PRIOR to USCOTS by filling out the Data Science Topics Survey at https://winona.az1.qualtrics.com/jfe/form/SV_6XQXkhwjrjcFhuS?utm_source=DS_Topics&utm_medium=Qualtrics)


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