Schedule

THIS IS AN INCOMPLETE DRAFT SCHEDULE

Submit 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 positron.tutorials, r4ds.tutorials, primer.tutorials, and tidycensus.tutorials for installation instructions.

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

remove.packages("primer.tutorials")
remotes::install_github("PPBDS/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 tutorials-in-positron_answers.html. Avoid using the name which will be assigned to the file if you download the same answers twice (or more), stuff like tutorials-in-positron_answers (2).html. You can just change the name of the file by hand if this happens.

Registration

Do not start the registration process yet.

Week 1: June 16

Introduction to the tools we use for doing data science.

Monday

Note that Monday’s homework must be completed before Monday’s class. 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 r4ds.tutorials package, complete the “Introduction” (00-introduction) tutorial.

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

Wednesday

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

  • Read Chapters 1 - 3 from R for Data Science (2e).

  • From the r4ds.tutorials package, complete the “Data visualization” (01-data-visualization) and “Data transformation” (03-data-transformation) 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

Week 2: June 23

Going forward, we recommend that you read the chapters in R for Data Science (2e) associated with each tutorial we assign from the r4ds.tutorials package. However, we stop listing them explicitly.

Finish learning about tools like Quarto and GitHub Pages. Start learning about models and inference.

Monday

Wednesday

  • Read Chapter 2 Probability and complete the “Probability” tutorial (021-probability) from the primer.tutorials package.

  • From the positron.tutorials package, complete the “Quarto Websites Introduction” (06-websites-1) tutorial.

  • From the r4ds.tutorials package, complete the “Exploratory data analysis” (10-exploratory-data-analysis) and “Communication” (11-communication) tutorials.

Thursday

Week 3: June 30

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

Monday

Wednesday

Thursday

Week 4: July 7

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

  • From the primer.tutorials package, complete the “Project” tutorial before class, or you will be removed the course. There are no extensions for this assignment.

  • 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 14

Renewed focus on models for inference.

Monday

Wednesday

Thursday

Week 6: July 21

Monday

Wednesday

Thursday

Week 7: July 28

Monday

Wednesday

Thursday

Week 8: August 4

Monday

  • From the primer.tutorials package, complete the “Project” tutorial before class, or you will be removed the course. There are no extensions for this assignment.

  • 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.