Prerequisites

In this book, we assume the reader is comfortable with the knowledge presented in Data 8 or some equivalent. In particular, we will assume that the reader is familiar with the following topics (links to pages from the Data 8 textbook are given in parentheses).

  • Tabular data manipulation: selection, filtering, grouping, joining (link)

  • Basic probability concepts (link)

  • Sampling, empirical distributions of statistics (link)

  • Hypothesis testing using bootstrap resampling (link)

  • Least squares regression and regression inference (link)

  • Classification (link)

In addition, we assume that the reader has taken a course in computer programming in Python, such as CS61A or some equivalent. We will not explain Python syntax except in special cases.

Finally, we assume that the reader has basic familiarity with partial derivatives, gradients, vector algebra, and matrix algebra.