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Sponsored by IASE

Instructors

Affiliated with Duke University, USA, and RStudio

Dr. Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel is Associate Professor of the Practice position at the Department of Statistical Science at Duke University and Data Scientist and Professional Educator at RStudio. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. She organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of the website Data Science in a Box, and she teaches the popular Statistics with R MOOC on Coursera.

Dr. Colin Rundel

Colin Rundel has been teaching statistics and data science courses, with a focus on computing and spatial modeling, for the last 8 years. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.

Course description

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 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.

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. Workshop attendees will work through several exercises from existing courses and get first-hand experience with using relevant tool-chains and techniques, including running a course on RStudio Cloud, and literate programming with R Markdown, and workflows for collaboration, version control, and automated feedback with Git/GitHub. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools.

Target audience

This workshop is aimed primarily at participants teaching data science in an academic setting in semester-long courses, however much of the information and tooling we introduce is applicable for shorter teaching experiences like workshops and bootcamps as well. Basic knowledge of R is assumed and familiarity with Git is preferred.

Syllabus

  • Curriculum design
  • Teaching the Tidyverse
  • Computing infrastructure with RStudio Cloud
  • Reproducible workflows: R Markdown, Git, GitHub
  • Interactivity and automated feedback
  • #rstats lifehacks for instructors

Required software

R and RStudio, however participants will also have the option to carry out the exercises in RStudip Cloud so they don’t need to set up anything particularly for the workshop.