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Abstract:
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Given that data science has embraced open source in such a big way, we believe that teaching data science should include teaching with open source data science practices and tools, as well as sharing open source educational resources. This follows the old adage, practice what you preach. Open source educational resources have many additional benefits that extend beyond the field of data science, including cost savings for learners, quick iteration on materials for instructors, and raising the quality of the resources by facilitating collaboration. In this talk, I will discuss open source educational resources for data science that we have created at the University of British Columbia that were also built authentically using open source data science practices and tools (e.g., R, {bookdown}, Python, Jupyter, Git & GitHub), these include: 1) "Data Science: A First Introduction" - an open textbook aimed at undergraduates students taking their first course in data science, 2) syllabi, lecture notes, labs and lecture videos from courses for a professional Master's in Data Science program, and 3) interactive online learning modules aimed at mid-career learners.
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