The Art of Data Science A Guide for Anyone Who Works with Data Roger D. Peng and Elizabeth Matsui This book is for sale at http://leanpub.com/artofdatascience This version was published on 2016-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. © 2015 - 2016 Skybrude Consulting, LLC Also By Roger D. Peng R Programming for Data Science Exploratory Data Analysis with R Executive Data Science Report Writing for Data Science in R Conversations On Data Science Special thanks to Maggie Matsui, who created all of the artwork for this book. Contents 1. Data Analysis as Art . . . . . . . . . . . . . . . . . 1 2. Epicycles of Analysis . . . . . . . . . . . . . . . . . 2.1 Setting the Scene . . . . . . . . . . . . . . . . 2.2 Epicycle of Analysis . . . . . . . . . . . . . . 2.3 Setting Expectations . . . . . . . . . . . . . . 2.4 Collecting Information . . . . . . . . . . . . 2.5 Comparing Expectations to Data . . . . . . 2.6 Applying the Epicyle of Analysis Process . . 4 5 6 8 9 10 11 3. Stating and Refining the Question . . . . . . . . 3.1 Types of Questions . . . . . . . . . . . . . . . 3.2 Applying the Epicycle to Stating and Refining Your Question . . . . . . . . . . . . . . . 3.3 Characteristics of a Good Question . . . . . 3.4 Translating a Question into a Data Problem 3.5 Case Study . . . . . . . . . . . . . . . . . . . . 3.6 Concluding Thoughts . . . . . . . . . . . . . 16 16 4. 20 20 23 26 30 Exploratory Data Analysis . . . . . . . . . . . . . 31 4.1 Exploratory Data Analysis Checklist: A Case Study . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Formulate your question . . . . . . . . . . . 33 4.3 Read in your data . . . . . . . . . . . . . . . . 35 4.4 Check the Packaging . . . . . . . . . . . .

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