As statistics educators, it is often easier to focus our teaching on methods instead of communication. And while many of us understand the value of good communication, actually teaching it is difficult and outside of our comfort zone. There has been quite a bit of work done on the science of visualization (e.g., the Grammar of Graphics by Wilkinson). There is general consensus that teaching students to communicate using visualizations is of paramount importance (see recent blog entries: National Academies Report on Data Science and GAISE).

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When people ask about how to get their students engaged with R in their introductory statistics and data science courses we offer three pieces of advice: keep it simple (discussed in the “Less Volume, More Creativity” blog entry) engage students to provide peer-tutoring and drop-in office hours to assist with questions and coding to complement class and office hours (at Amherst College this is coordinated by the Statistics and Data Science Fellows) have students use a dedicated server to access R Slide credit: Mine Çetinkaya-Rundel

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Pair programming is a technique from software development where two programmers work in tandem to code. One is designated the driver, responsible for typing, while the other, often called the navigator or observer reviews the code and provides a high-level overview of the task. Photo credit: Esti Alvarez Pair programming has been thought to lead to better code, more enjoyable coding, and higher productivity, with some research findings supporting those conclusions (see some of the references at the end of this entry).

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In 2017, Jenny Bryan and Hadley Wickham published the “Practical Data Science for Stats” PeerJ collection. (The papers were also published in a special issue of The American Statistician.) The “Practical Data Science for Stats” Collection contains a series of short papers focused on the practical side of data science workflows and statistical analysis. There are many aspects of day-to-day data analytical work that are almost absent from the conventional statistics literature and curriculum.

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In 2016, GAISE enunciated the importance of multivariate thinking and technology when teaching introductory statistics and data science courses. A big challenge is how to do this using R and RStudio without running into cognitive overload with our students. The mosaic package was created by Randall Pruim, Danny Kaplan, and Nicholas Horton with the goal of introducing a Less Volume, More Creativity approach to introductory statistics that could simplify the use of technology.

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