Schedule

Classes run from 8:00 to 9:00 PM EDT on Mondays, Wednesdays and Thursdays.

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, along with their their associated tutorials. Homework should be completed before the class meeting for which it is assigned.

See the home pages for the r4ds.tutorials, primer.tutorials, and tidycensus.tutorials for installation instructions.

Always reinstall the relevant package before starting a new tutorial. We are always fixing mistakes. You want to use the latest version.

Completed tutorials are submitted via this Google form. 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 getting-started_answers.html. Avoid using the name which will be assigned to the file if you download the same answers twice (or more), stuff like getting-started_answers (2).html. You can just change the name of the file by hand if this happens.

Registration

To register for the class and reserve your spot, please read Getting Started and follow all the associated instructions. Complete the “Getting Started with Tutorials” tutorial from the tutorial.helpers package. Submit your answers, as an html file, via this Google form.

Week 1: Data: June 3

Looking at the data is the first step in data science. 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.

Monday

Wednesday

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

  • From the r4ds.tutorials package, complete two tutorials: “Data Visualization” and “RStudio and Code.” The second of these is the most important. Submit your answers via the usual Google form.

Thursday

  • Read Chapters 3 and 4 from R for Data Science (2e).

  • From the r4ds.tutorials package, complete the associated tutorials: “Data Transformation” and “RStudio and Github.” The most important of these is “RStudio and Github.”

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.

Week 2: Models: June 10

Now that we know how to visualize data that is already clean, we need to learn how to clean up messy data.

Monday

  • Read Chapters 5, 6, and 7 from R for Data Science (2e), and from the r4ds.tutorials package, complete the tutorials associated with the readings: “Data Tidying,” “Terminal,” and “Data Import.”

Wednesday

  • Read Chapters 8, 9 and 10 from R for Data Science (2e), and from the r4ds.tutorials package, complete the tutorials associated with the readings: “Getting Help,” “Layers,” and “Exploratory Data Analysis.”

Thursday

The tidycenus.tutorials are ready. You must install them with: remotes::install_github("PPBDS/tidycensus.tutorials").

Week 3: Inference: June 17

Monday

  • Read chapters 12, 28 and 29 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Logical vectors” and “Quarto” tutorials.

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

Wednesday

Thursday

Week 4: Wisdom: June 24

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

Wednesday

  • Read chapter 18 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Missing values” tutorial.

  • 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. The presentations are public.

TWO WEEK VACATION: July 1 through July 14

The primer.tutorials are not yet complete. Don’t start them yet.

Week 5: Justice: July 15

Monday

Wednesday

Thursday

Week 6: Courage: July 22

Monday

Wednesday

Thursday

Week 7: Temperance: July 29

Monday

Wednesday

Thursday

Week 8: Projects: August 5

The last week will consist of projects that the students will make.

Monday

  • From the primer.tutorials package, complete “Final 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 ready to go, including a Quarto website and your four sentence 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.