Preface
1
Tools
1.1
Useful Resources
1.2
Why these tools?
1.3
R and RStudio
1.3.1
rstudio.calvin.edu
1.3.2
RStudio on the Linux machines
1.3.3
Rstudio on your own machine
1.4
R packages
2
Visualization
2.1
Reading
2.1.1
Why
2.1.2
How
2.2
Application
2.3
References
2.4
Tweaks
2.4.1
Reordering bars in a bar plot
2.4.2
Tweaking scales
2.4.3
Direct Labels
2.4.4
Legends and Labels
2.5
Mapping
2.5.1
Plotly
3
Data Wrangling
3.1
Resources
3.1.1
Practice
3.2
SQL and BigQuery
3.3
Afterward
4
Predictive Modeling
4.1
Lingo
4.2
Reading Guide
4.2.1
Prediction as a Goal
4.2.2
Linear models for regression
4.2.3
tidymodels
4.3
Modeling Goals
4.4
Defining Overfitting
5
Other Topics
5.1
Text Mining (and bias)
5.2
Resources
6
Communication
6.1
Resources
DATA 202 Fall 2020
5
Other Topics
5.1
Text Mining (and bias)
Tidy Text Mining
(link is to a specific interesting section)
How to make a racist AI without really trying
and a
follow-up in R
5.2
Resources
ACM Selects:
Algorithmic Fairness
Data Science