This book is meant to accompany DSCI 335. It is not a complete textbook; you will need to take notes on what you hear in class and what you read throughout the semester. In it, you will find:

  • An outline of the course topics
  • Links to readings and videos, both required and recommended
  • Notes on course topics that are not covered in readings and videos
  • Example R code for demonstrating concepts (often via simulation, which is a major component of the course)

I will be adding “chapters” to this book as the semester progresses.

Course topics outline

  1. What this class is about
  2. Classical inference
  3. Bayesian vs. frequentist probability
  4. Estimation vs. testing
  5. Quantifying magnitude
  6. Correlation, causation, and statistical control
  7. Violating model assumptions
  8. Data ethics