Executive Data Science
Executive Data Science
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Executive Data Science

Last updated on 2018-12-12

About the Book

In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

If you are interested in a printed copy of this book, you can purchase one at Lulu.

About the Authors

Brian Caffo
Brian Caffo

Brian Caffo, PhD is a professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Along with Roger Peng and Jeff Leek, Dr. Caffo created the Data Science Specialization on Coursera. Dr. Caffo is leading  expert in statistics and biostatistics and is the recipient of the PECASE award, the highest honor given by the US Government for early career scientists and engineers.

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 the author of the popular book R Programming for Data Science and nine 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.

Jeffrey Leek
Jeffrey Leek

Jeff Leek is a Professor of Biostatistics and Oncology at Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. He writes for the blog Simply Statistics and can be found on Twitter @jtleek@simplystats.

Table of Contents

  • A Crash Course in Data Science
    • 1. What is Data Science?
      • Moneyball
      • Voter Turnout
      • Engineering Solutions
    • 2. What is Statistics Good For?
    • 3. What is Machine Learning?
    • 4. What is Software Engineering for Data Science?
    • 5. Structure of a Data Science Project
    • 6. Output of a Data Science Experiment
    • 7. Defining Success: Four Secrets of a Successful Data Science Experiment
    • 8. Data Science Toolbox
    • 9. Separating Hype from Value
  • Building the Data Science Team
    • 10. The Data Team
    • 11. When Do You Need Data Science?
      • The Startup
      • The Mid-Sized Organization
      • Large Organizations
    • 12. Qualifications & Skills
      • Data Engineer
      • Data Scientist
      • Data Science Manager
    • 13. Assembling the Team
      • Where to Find the Data Team
      • Interviewing for Data Science
    • 14. Management Strategies
      • Onboarding the Data Science Team
      • Managing the Team
    • 15. Working With Other Teams
      • Embedded vs. Dedicated
      • How Does Data Science Interact with Other Groups?
      • Empowering Others to Use Data
    • 16. Common Difficulties
      • Interaction Difficulties
      • Internal Difficulties
  • Managing Data Analysis
    • 17. The Data Analysis Iteration
      • Epicycle of Analysis
    • 18. Asking the Question
      • Types of Questions
    • 19. Exploratory Data Analysis
    • 20. Modeling
      • What Are the Goals of Formal Modeling?
      • Associational Analyses
      • Prediction Analyses
    • 21. Interpretation
    • 22. Communication
  • Data Science in Real Life
    • 23. What You’ve Gotten Yourself Into
      • Data double duty
      • Multiplicity
      • Randomization versus observational studies
    • 24. The Data Pull is Clean
    • 25. The Experiment is Carefully Designed: Principles
      • Causality
      • Confounding
    • 26. The Experiment is Carefully Designed: Things to Do
      • A/B testing
      • Sampling
      • Blocking and Adjustment
    • 27. Results of the Analysis Are Clear
      • Multiple comparisons
      • Effect sizes, significance, modeling
      • Comparison with benchmark effects
      • Negative controls
    • 28. The Decision is Obvious
      • The decision is (not) obvious
      • Estimation target is relevant
    • 29. Analysis Product is Awesome
    • About the Authors
  • Notes

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