Essays on Data Analysis
Essays on Data Analysis
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Essays on Data Analysis

Last updated on 2019-05-01

About the Book

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.

About the Author

Roger D. Peng
Roger D. Peng

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He is also a co-founder of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the general public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. He is a Fellow of the American Statistical Association and is the recipient of the 2016 Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. Roger can be found on Twitter and GitHub at @rdpeng.

Table of Contents

  • I Defining 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
  • II Data Analytic Process
    • 8. Divergent and Convergent Phases of Data Analysis
    • 9. Abductive Reasoning
    • 10. Tukey, Design Thinking, and Better Questions
    • 11. The Role of Creativity
    • 12. Should the Data Come First?
    • 13. Partitioning the Variation in Data
    • 14. How Data Analysts Think - A Case Study
  • III Human Factors
    • 15. Trustworthy Data Analysis
    • 16. Relationships in Data Analysis
    • 17. Being at the Center
  • IV Towards a Theory
    • 18. The Role of Theory in Data Analysis
    • 19. The Tentpoles of Data Science
    • 20. Generative and Analytical Models
  • Notes

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