Workshop A1 (Saturday 7th, 09:30-13:30; Room: 2-AVS)
Teaching Data Science, Reproducibly (Part 1)
Success in data science and statistics is dependent on the development of both analytical and computational skills. As statistics educators we are more familiar and comfortable with teaching the former, but the latter is becoming increasingly important. The goal of this workshop is to equip educators with concrete information on content and infrastructure for painlessly introducing modern computation into a data science and/or statistics curriculum. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into choosing infrastructure and developing curriculum. Workshop attendees will work through several exercises from existing courses and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, and collaboration, version control, and automated feedback with git/GitHub.
This workshop is aimed at participants who are interested in the role of computing in either a Statistics or Data Science curriculum. This includes faculty who are designing new courses or programs as well as those who are interested in adding or improving a computational component to an existing course.
The workshop will be comprised of two 1/2 day parts, part 1 will introduce teaching data science and statistics courses using R and RStudio and part 2 will focus on best practices for configuring and deploying infrastructure to support these tools along with a version control system in the classroom.
Bring your own laptop.
Part 2 of this workshop (Workshop A2) will be held the following day.
For questions and additional information, contact the organizer/presenter above.