Abstract
We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues raised should be relevant to nearly all statistics instructors. Supplementary materials for this article are available online.
Highlights
Online education continues to put pressure on brick-andmortar institutions to deliver on the promise of a better life for our graduates
We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course
We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process
Summary
Online education continues to put pressure on brick-andmortar institutions to deliver on the promise of a better life for our graduates. We have had experience teaching on a variety of online learning platforms, including Coursera, edX, and Statistics.com, this article focuses on our experience with DataCamp. DataCamp is an online learning platform whose curriculum is centered around data science, targeting coding skills in R, Python, and SQL. We reflect on our experience designing an eight-course sequence that comprises the Statistics Fundamentals with R1 and Statistical Inference with R skills tracks on DataCamp.
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