TMaterials for the “Computing Infrastructure and Curriculum Design for Introductory Data Science” workshop at SIGCSE 2019 in Minneapolis, MN.

See the GitHub repo here, and the RStudio Cloud space here.

7:00 PM - 10:00 PM
Wed Feb 27, 2019
Hyatt: Greenway F (2nd floor)

Interested in teaching introductory data science? running your course on GitHub, and doing so efficiently? what first exposure to computing with R might look like? what the tidyverse is? If your answer is yes to any of these, this workshop is for you! We will showcase and discuss the pedagogical considerations behind the introductory data science curriculum presented in Data Science in a Box (datasciencebox.org), get hands on practice with tooling, and share a complete set of open source course materials, including teacher facing documentation and student facing learning resources and assessments.

The goal of this workshop is to equip educators with concrete information on content and infrastructure for designing and painlessly running a modern data science course. This is a three-part workshop.

• Part 1 will outline a curriculum for an introductory data science course and discuss pedagogical decisions that go into the choice of topics and concepts, programming language (R) and syntax (primarily tidyverse), emphasis on literate programming for reproducibility (with R Markdown).
• Part 2 will discuss infrastructure choices around teaching data science with R: RStudio as an integrated development environment, cloud-based access with RStudio Cloud and Server, version control with Git, and collaboration with GitHub.
• Part 3 will focus on classroom management on GitHub (with ghclass).

Workshop attendees will work through several exercises from the course and get first-hand experience with using the tool-chains and techniques described above. All workshop content, including teacher facing documentation and student facing course materials, will also be available to participants via datasciencebox.org.

• Welcome
• Part 1 - Curriculum
• Part 2 - Infrastructure
• Part 3 - Classroom management
  • Demo

In this workshop we will be coding in R via the RStudio IDE. If the conference WiFi cooperates we will do this in the cloud via rstudio.cloud. I will purposefully hold off instructions for this so that you can experience students’ day one experience in the class.

However, the backup option, in case WiFi doesn’t cooperate, is running RStudio locally. Below are the instructions for the required local setup. Note that these instructions are a lot more detailed than what the students in the intro data science course are exposed to.

• Install R: A recent version of R (>= 3.5.2 “Eggshell Igloo”) is recommended. If you already have R installed, you can check your version from the R Console:

getRversion()

## [1] '3.5.2'

• Install RStudio: A recent version of RStudio (>= v1.2.1293-1) is recommended.

• Install packages: For this workshop, you’ll need to install several R packages. To do so, please run the following in the your R console:

workshop_pkgs <- c("tidyverse", "openintro",  "infer", "broom")
install.packages(workshop_pkgs)

• Set up Git/GitHub:
  • Register a GitHub account
  • Install Git
  • Introduce yourself to Git
  • Connect to GitHub
  • Connect RStudio to Git and GitHub