The Elements of Data Analytic Style

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials. The author is one of the co-developers of the Johns Hopkins Specialization in Data Science the largest data science program in the world that has enrolled more than 1.76 million people. The book is useful as a companion to introductory courses in data science or data analysis. It is also a useful reference tool for people tasked with reading and critiquing data analyses. It is based on the authors popular open-source guides available through his Github account (https://github.com/jtleek).

The paper is also available through Leanpub (https://leanpub.com/datastyle), if the book is purchased on that platform you are entitled to lifetime free updates.

Table of Contents

Chapter 1. Introduction
Chapter 2. The data analytic question
Chapter 3. Tidying the data
Chapter 4. Checking the data
Chapter 5. Exploratory analysis
Chapter 6. Statistical modeling and inference
Chapter 7. Prediction and machine learning
Chapter 8. Causality
Chapter 9. Written analyses
Chapter 10. Creating figures
Chapter 11. Presenting data
Chapter 12. Reproducibility
Chapter 13. A few matters of form
Chapter 14. The data analysis checklist
Chapter 15. Additional resources