Schedule

DRAFT: Will be revised in over the coming weeks. Mostly leftover from last year.

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: R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, Preceptor’s Primer for Bayesian Data Science by David Kane, Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker.

See the home pages for vscode.tutorials and primer.tutorials.

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

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. I 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 tutorial.helpers package, complete the “Introduction to R” tutorial.

  • From the vscode.tutorials package, complete the “Code” (01-code) tutorial. Make sure that you have already completed the “Introduction to R” tutorial above before you start the “Code” tutorial.

Wednesday

  • From the vscode.tutorials package, complete the “Quarto” (02-quarto) and “Terminal” (03-terminal) tutorials.

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. Start learning about models and inference.

Monday

Wednesday

Thursday

Week 3: June 29

Identify the data which you will use for your Data Project. The U.S. Census is a great source.

Monday

Note that chapters 4 and higher from the Primer are a mess, using packages/functions which we no longer recommend. We are re-writing the Primer this summer. In the meantime, don’t feel obligated to read any more of it, although you do have to complete the tutorials, which are up-to-date. We just list the chapters in this schedule for your reference.

  • Complete the “Models” tutorial (041-models) from the primer.tutorials package.

  • Read chapters 1 – 3 from from Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker.

  • From the tidycensus.tutorials package, complete the “An introduction to tidycensus” (02-introduction) and “Wrangling Census data with Tidyverse tools” (03-wrangling) tutorials.

  • Come to class with the url for at least one data source you will be using for your Data Project.

Wednesday

Thursday

  • Complete the “Three Parameters: Causal” tutorial (061-three-parameters-causal) from the primer.tutorials package.

Week 4: July 6

The main focus on the 4th week is individual presentations of your Data Project. Do you love soccer or wine or NYC politics? The Data Project provides you with an opportunity to study that topic in depth. Your Data Project will be, for most of you, the first item in your professional portfolio, something so impressive that you will be eager to show it to graduate schools or potential employers.

Monday

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

Wednesday

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

Thursday

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

Week 5: July 13

Renewed focus on models for inference.

Monday

  • Complete the “Mechanics” tutorial (071-mechanics) from the primer.tutorials package.

Wednesday

  • Complete the “Four Parameters: Categorical” tutorial (081-four-parameters-categorical) from the primer.tutorials package.

Thursday

  • Complete the “Five Parameters” tutorial (091-five-parameters) from the primer.tutorials package.

Week 6: July 20

Monday

  • Complete the “N Parameters” tutorial (101-n-parameters) from the primer.tutorials package.

Wednesday

  • Complete the “Cumulative” tutorial (111-cumulative) from the primer.tutorials package.

Thursday

  • Complete the “Stops” tutorial (131-stops) from the primer.tutorials package.

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 and your paragraph introduction.

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.