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, and their associated tutorials. Homework should be completed before the class meeting for which it is assigned. We also reference Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker.
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.
Week 1: Data
Looking at the data is the first step in data science.
Monday
- Before class, please read Getting Started and follow all the associated instructions. Complete the “Getting Started” tutorial from the tutorial.helpers package. Submit your answers, as an html file, via this Google form.
Wednesday
Read the Introduction and Chapters 1 and 2 from R for Data Science (2e).
From the r4ds.tutorials package, complete three tutorials: Introduction, Data Visualization and RStudio and Code. The third of these is the most important. Submit your answers via the usual Google form. (Note that you need to submit each set of answers separately.)
Thursday
Read Chapters 3, 4, and 5 from R for Data Science (2e).
From the r4ds.tutorials package, complete the associated tutorials: Data Transformation, RStudio and Github, and Data Tidying tutorials. 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
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 6, 7 and 8 from R for Data Science (2e).
From the r4ds.tutorials package, complete the Terminal tutorial. Also complete the tutorials associated with the readings: Data Import and Getting Help.
Wednesday
Read chapters 12, 13, 14 and 15 from R for Data Science (2e).
From the r4ds.tutorials package, complete the associated tutorials: Logical vectors, Numbers, Strings, and Regular expressions.
Thursday
Read Chapters 9, 10 and 11 from R for Data Science (2e).
From the r4ds.tutorials package, complete the tutorials associated with the readings: Layers, Exploratory Data Analysis, and Communication.
Week 3: Inference
Now that we know how to clean and organize messy data, we need to learn how to get some data.
Monday
Read chapters 16, 17, 18 and 19 from R for Data Science (2e).
From the r4ds.tutorials package, complete the associated tutorials: Factors, Dates and times, Missing values, and Joins.
Wednesday
Read chapters 20 and 21 from R for Data Science (2e).
From the r4ds.tutorials package, complete the associated tutorials: Spreadsheets and Databases.
Read the Style Guide.
Determine the data source you will use for your final project. Don’t come to class without one!
Thursday
Read chapters 24 and 28 from R for Data Science (2e).
From the r4ds.tutorials package, complete the associated tutorials: Web scraping and Quarto. The latter is much more important.
Week 4: Wisdom
Monday
Read Chapter 1 Rubin Causal Model. Focus on sections 1.1, 1.2 and 1.4.
From the primer.tutorials package, complete Rubin Causal Model - Overview tutorial.
From the tidycensus.tutorials package, complete Census Data I.
Wednesday
Read Chapter 2 Probability. Focus on sections 2.1, 2.2, 2.7, and 2.8.
From the primer.tutorials package, complete Probability - Overview tutorial.
From the tidycensus.tutorials package, complete Census Data II.
Thursday
- From the tidycensus.tutorials package, complete API Key.
Week 5: Justice
Monday
TBD
Wednesday
TBD
Thursday
- Read chapter 24 from R for Data Science (2e). From the r4ds.tutorials package, complete the associated tutorial: Hierarchical data.
Week 6: Courage
Monday
TBD
Wednesday
TBD
Thursday
TBD
Week 7: Temperance
Monday
TBD
Wednesday
TBD
Thursday
- Read Key Concepts.
Week 8: Projects
The last week will consist of projects that the students will make. Do you love soccer or wine or NYC politics? The final project provides you with an opportunity to study that topic in depth. Your final 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 “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.