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Abstract:
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Teaching computing is too often treated as an after thought in statistics curricula, where students are expected to pick up the necessary tools and techniques along the way. With the rise of big data and data science it has become increasingly clear that students both want, expect, and need training in this specific area of the discipline. In this talk we will discuss the specific efforts we have made at Duke Statistical Science to meet these demands in the form of an upper-division elective, Sta 323 - Statistical Computing. The overarching goal in this course is to introduce and help students adopt the tools and best practices for reproducible, reliable, and high quality scientific computing, which in many cases borrows heavily from disciplines like computer and data science and software engineering. The course is heavily programming based, using the R language, and students engage with a wide range of tasks from algorithm implementation and testing to web-scraping to model building, validation, and visualization. All of this is undertaken within a team-based setting with an emphasis on collaborative problem solving.
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