The dramatic change in the price and accessibility of data demands a new focus on data analytic literacy. This book is intended for use by people who perform regular data analyses. It aims to give a brief summary of the key ideas, practices, and pitfalls of modern data analysis. One goal is to summarize in a succinct way the most common difficulties encountered by practicing data analysts. It may serve as a guide for peer reviewers who may refer to specific section numbers when evaluating manuscripts. As will become apparent, it is modeled loosely in format and aim on the Elements of Style by William Strunk.
The book includes a basic checklist that may be useful as a guide for beginning data analysts or as a rubric for evaluating data analyses. It has been used in the author's data analysis class to evaluate student projects. Both the checklist and this book cover a small fraction of the field of data analysis, but the experience of the author is that once these elements are mastered, data analysts benefit most from hands on experience in their own discipline of application, and that many principles may be non-transferable beyond the basics.
This open book is licensed under a Creative Commons License (CC BY). You can download The Elements of Data Analytic Style ebook for free in PDF format (1.8 MB).
Table of Contents
The data analytic question
Tidying the data
Checking the data
Statistical modeling and inference
Prediction and machine learning
A few matters of form
The data analysis checklist