Contents
Fifteen chapters across five units. Each chapter runs two or three lectures of about ninety minutes, and each lecture carries its own practice and assignment.
A Foundations
Getting R running, then writing code and handling data well enough to start asking questions of it.
Alongside this unit: Foundations of Data Science, a full course on R and the tidyverse
B Inference
Where an estimate comes from, how much of it to believe, and how to compare two or more groups.
C The Linear Model
One framework, regression, stretched to cover categorical predictors, interactions, and curves.
D Data Problems and Robust Inference
What to do when the assumptions those models rest on do not hold, and how to tell that they do not.
E Designing and Reporting Studies
Measuring well, planning a study large enough to answer its question, and reporting what came back.
After this course: Generalized Linear Models (coming soon), then Multilevel Modeling