Schedule

Submit each of your homework answers, as an html file, via this Google form before midnight on the day before the class for which the homework was assigned.

Key resources: Preceptor’s Primer for Bayesian Data Science, vscode.tutorials, and primer.tutorials.

Always reinstall the relevant package before starting a new tutorial. Example:

remove.packages("vscode.tutorials")
remotes::install_github("PPBDS/vscode.tutorials")

primer.tutorials is a little trickier:

remove.packages("primer.tutorials")
remotes::install_github("PPBDS/primer", subdir = "primer.tutorials")

We are always fixing mistakes. You want to use the latest version.

If you do not complete the tutorials on time, you will be removed from the class. Send in your tutorial answers saved in html format. Please try to ensure that the name of the file is the default name, like 01-code_answers.html. Avoid using the name which will be assigned to the file if you download the same answers twice (or more), stuff like 01-code_answers (2).html. You can just change the name of the file by hand if this happens.

Week 1: June 15

Introduction to the tools we use for doing data science.

Monday

Monday’s homework must be completed before midnight on Sunday. We ensure this by only sending the Zoom link for class to students who submitted the homework.

  • Read the Introduction from R for Data Science (2e).

  • From the vscode.tutorials package, complete the “Code” (01-code) and “Quarto” (02-quarto) tutorials. Make sure that you have already completed the “Introduction to R” tutorial from tutorial.helpers before you starting these tutorials. We complete the tutorials from the vscode.tutorials in the order in which they appear.

Wednesday

  • From the vscode.tutorials package, complete the “Terminal” (03-terminal) tutorial.

Reminder: All work must be completed before class. Failure to submit your tutorial answers will result in you being removed from the course. It is not fair to your fellow students, with whom you will be working in small groups, for you to not be prepared for class.

Thursday

  • From the vscode.tutorials package, complete the “GitHub Introduction” (04-github-1) tutorial.

Week 2: June 22

Finish learning about tools like Quarto and GitHub Pages.

Monday

  • From the vscode.tutorials package, complete the “GitHub Advanced” (05-github-2) and “AI” (06-ai) tutorials.

Wednesday

  • From the misc.tutorials package, complete the “R for Data Science 1” (r4ds-1) tutorial.

Thursday

  • From the vscode.tutorials package, complete the “Quarto Websites Introduction” (07-websites-1) tutorial.

Week 3: June 29

Monday

  • From the misc.tutorials package, complete the “R for Data Science 2” (r4ds-2) and “Census” (census) tutorials.

Wednesday

  • From the vscode.tutorials package, complete the “Quarto Websites Advanced” (08-websites-2) tutorial.

Thursday

Week 4: July 6

Monday

Wednesday

Thursday

Week 5: July 13

Monday

  • From the misc.tutorials package, complete the “R for Data Science 3” (r4ds-3) tutorial.

Wednesday

Thursday

  • From the misc.tutorials package, complete the “R for Data Science 4” (r4ds-4) tutorial.

Week 6: July 20

Monday

Wednesday

Thursday

  • From the misc.tutorials package, complete the “R for Data Science 5” (r4ds-5) tutorial.

Week 7: July 27

Monday

Wednesday

Thursday

Week 8: August 3

Monday

  • You must have a draft of your final project, including a Quarto website.

Wednesday

  • Practice presentations and feedback. You must have your presentation ready to go!

Thursday

  • Final project presentations. You must invite someone, and bcc your TF when you do so.