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.
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A Crash Course in Data Science
1. What is Data Science?
- 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
- 1. What is Data Science?
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
- What Are the Goals of Formal Modeling?
- Associational Analyses
- Prediction Analyses
- 21. Interpretation
- 22. Communication
- 17. The Data Analysis Iteration
Data Science in Real Life
23. What You’ve Gotten Yourself Into
- Data double duty
- Randomization versus observational studies
- 24. The Data Pull is Clean
25. The Experiment is Carefully Designed: Principles
26. The Experiment is Carefully Designed: Things to Do
- A/B testing
- 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
- 23. What You’ve Gotten Yourself Into
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