As you go, you may develop your own R Markdown style you like or you might use my file layout to start. If you’d like a different starting point, you can use the template below.
Fall 2021 Assignments
Assignment #1
Goals:
- Set up and use git and GitHub with RStudio.
- Create a personal website and hear about why this is a useful thing to have.
- Start generating ideas about your project for the course.
- Read about and reflect on bias and fairness in data science.
Assignment #2
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Refresh your machine learning skills.
- Use the
tidymodels packages to build models.
- Read about and reflect on bias and fairness in data science.
Assignment #3
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Build more machine learning models using
tidymodels, including xgboost.
- Build and use a stacked model.
- Start thinking about a shiny app.
- Explore new packages from “Function Friday”.
- Continue learning about bias and fairness in data science.
Assignment #4
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Use SQL inside of R.
- Make some amazing graphs!
- Create a shiny app using the slightly more advanced Shiny tips we covered in class.
- Explore new packages from “Function Friday”.
Assignment #5
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Use some global model interpretation tools.
- Create a shiny app that allows you to interact with a model.
- Use some local model interpretation tools.
- Continue learning about bias and fairness in data science.
Spring 2021 Assignments
Assignment #1
Goals:
- Set up and use git and GitHub with RStudio.
- Create a personal website and be motivated to do it.
- Create a small package (optional).
- Refresh your machine learning skills.
- Use the
tidymodels packages to build models.
- Read about and reflect on bias and fairness in data science.
Assignment #2
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Build more machine learning models using
tidymodels.
- Build and use a stacked model.
- Use some global model interpretation tools.
- Create a shiny app that allows you to interact with a model.
- Add some customized theming to a shiny app.
- Continue learning about bias and fairness in data science.
Assignment #3
Goals:
- Continue using and become more comfortable with Git and GitHub.
- Use some local model interpretation tools.
- Use SQL inside of R.
- Make some amazing graphs!
- Practice using
geom_sf()
- Practice using
tidytext
- Continue learning about bias and fairness in data science.
Assignment #4
Goals:
- Practice using
tmap
- Practice using regular expressions via
stringr
- Continue learning about bias and fairness in data science.
Assignment #5
- Practice using
countrycode R package.