AI-Assisted Statistics for Data Scientists, 3rd Edition

Chapter 1. Exploratory Data Analysis

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 1st chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

This chapter focuses on the first step in any data science project: exploring the data.

Classical statistics focused almost exclusively on inference, a sometimes complex set of procedures for drawing conclusions about large populations based on small samples. In 1962, John W. Tukey (Figure 1-1) called for a reformation of statistics in his seminal paper “The Future of Data Analysis” [Tukey-1962]. He proposed a new scientific discipline called data analysis that included statistical inference as just one component. Tukey forged links to the engineering and computer science communities (he coined the terms bit, short for binary digit, and software), and his original tenets are surprisingly durable and form part of the foundation for data science. The field of exploratory data analysis was established with Tukey’s 1977 now-classic book Exploratory Data Analysis [Tukey-1977]. Tukey presented simple plots (e.g., boxplots, scatterplots) that, along with summary ...