Abstract:
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The new ASA Curriculum Guidelines for Undergraduate Programs in Statistical Science lists "data manipulation and computation" as a key skill to be developed in undergraduate courses. This talk proposes strategies for building proficiency in this skill via R beyond the first course. Appropriately, many students nowadays first encounter R in introductory statistics courses. In such courses, the learning objectives are diverse, and R is often only used to perform cookie-cutter data analyses or simple simulations. The second course provides a great opportunity for more real data experiences. Real data experiences may involve messy data, requiring any combination of scraping, cleaning, and substantial transforming; or they may involve messy problems, where approaches to analysis are not direct applications of textbook methods. This talk will provide examples of in-class activities to demonstrate how such real data experiences using R can be successfully used to supplement the other learning objectives in a second course.
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