In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into developing a data science curriculum and choosing workflows and infrastructure that best support the curriculum and allow for scalability. The goal of this workshop is to equip educators with concrete information on content, workflows, and infrastructure for painlessly introducing modern computation with R and RStudio within a data science curriculum. Success in data science and statistics is dependent on the development of both analytical and computational skills, and the demand for educators who are proficient at teaching both these skills is growing. The activities and demo will be hands-on attendees will also have the opportunity to exchange ideas and ask questions throughout the session. We’ll share example activities and assignments, along with a demo of the computing toolkit using the R tidymodels package, Quarto for reproducible reports, and Git and GitHub for version control and collaboration. We’ll share strategies for using real-world data sets and examples, teaching modern computing skills, and incorporating non-technical skills such as writing and effective collaboration as part of the course. In this session, we’ll present a modern approach to teaching undergraduate regression analysis, the second statistics course for many students.
Innovating subsequent courses is also important, so students can continue developing these skills beyond the first course. There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis.
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