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Executive Data Science

A Guide to Training and Managing the Best Data Scientists

This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization. Printed copies of this book are available through Lulu.

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About

About

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.

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Author

About the Authors

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.

Leanpub Podcast

Episode 21

An Interview with Brian Caffo

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.

Leanpub Podcast

Episode 16

An Interview with Roger D. Peng

Jeffrey Leek

Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.

Leanpub Podcast

Episode 19

An Interview with Jeffrey Leek

Contents

Table of Contents

IA Crash Course in Data Science

1.What is Data Science?

  1. Moneyball
  2. Voter Turnout
  3. 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

  1. IIBuilding the Data Science Team

10.The Data Team

11.When Do You Need Data Science?

  1. The Startup
  2. The Mid-Sized Organization
  3. Large Organizations

12.Qualifications & Skills

  1. Data Engineer
  2. Data Scientist
  3. Data Science Manager

13.Assembling the Team

  1. Where to Find the Data Team
  2. Interviewing for Data Science

14.Management Strategies

  1. Onboarding the Data Science Team
  2. Managing the Team

15.Working With Other Teams

  1. Embedded vs. Dedicated
  2. How Does Data Science Interact with Other Groups?
  3. Empowering Others to Use Data

16.Common Difficulties

  1. Interaction Difficulties
  2. Internal Difficulties
  3. IIIManaging Data Analysis

17.The Data Analysis Iteration

  1. Epicycle of Analysis

18.Asking the Question

  1. Types of Questions

19.Exploratory Data Analysis

20.Modeling

  1. What Are the Goals of Formal Modeling?
  2. Associational Analyses
  3. Prediction Analyses

21.Interpretation

22.Communication

  1. IVData Science in Real Life

23.What You’ve Gotten Yourself Into

  1. Data double duty
  2. Multiplicity
  3. Randomization versus observational studies

24.The Data Pull is Clean

25.The Experiment is Carefully Designed: Principles

  1. Causality
  2. Confounding

26.The Experiment is Carefully Designed: Things to Do

  1. A/B testing
  2. Sampling
  3. Blocking and Adjustment

27.Results of the Analysis Are Clear

  1. Multiple comparisons
  2. Effect sizes, significance, modeling
  3. Comparison with benchmark effects
  4. Negative controls

28.The Decision is Obvious

  1. The decision is (not) obvious
  2. Estimation target is relevant

29.Analysis Product is Awesome

About the Authors

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