Essays on Data Analysis

This book draws a complete picture of the data analysis process, filling out many details that are missing from previous presentations. It presents a new perspective on what makes for a successful data analysis and how the quality of data analyses can be judged.

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What is a data analysis? What makes for a successful data analysis? These are difficult questions that even long-time practitioners have difficulty answering. The way that we have thought about data analysis to date has been focused on the data and the statistical tools that we employ to produce results. But data analysis is about more than those things, and developing an understanding of the things "outside" the data is critical to characterizing the actual process of data analysis, the process that data analysts go through every day.

This book attempts to draw a more complete picture of the data analysis process and presents a new view about what makes for a successful data analysis. It is presented in a completely non-technical and highly readable style that should be of interest to practitioners and managers in data analysis.

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Roger D. Peng

Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.

Episode 16

An Interview with Roger D. Peng

Table of Contents

IDefining Data Analysis

1.The Question

2.What is Data Analysis?

3.Data Analysis as a Product

4.Context Compatibility

5.The Role of Resources

6.The Role of the Audience

7.The Data Analysis Narrative

8.The Four Jobs of the Data Scientist

IIData Analytic Process

9.Divergent and Convergent Phases of Data Analysis

10.Abductive Reasoning

11.Tukey, Design Thinking, and Better Questions

12.The Role of Creativity

13.Should the Data Come First?

14.Partitioning the Variation in Data

15.How Data Analysts Think - A Case Study

IIIHuman Factors

16.Trustworthy Data Analysis

17.Relationships in Data Analysis

18.Being at the Center

19.Economic Models for Reproducible Analysis

IVTowards a Theory

20.The Role of Theory in Data Analysis

21.The Tentpoles of Data Science

22.Generative and Analytical Models

23.Thinking About Failure in Data Analysis

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