Executive Data Science: A Guide To Training And Managing The Best Data Scientists

✍ Scribed by Brian Caffo, Roger D. Peng, Jeffrey T. Leek

• Publisher: lulu.com
• Year: 2018
• Tongue: English
• Leaves: 150
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Table of Contents......Page 2
A Crash Course in Data Science......Page 6
What is Data Science?......Page 7
Moneyball......Page 8
Engineering Solutions......Page 9
What is Statistics Good For?......Page 11
What is Machine Learning?......Page 13
What is Software Engineering for Data Science?......Page 16
Structure of a Data Science Project......Page 20
Output of a Data Science Experiment......Page 24
Defining Success: Four Secrets of a Successful Data Science Experiment......Page 27
Data Science Toolbox......Page 29
Separating Hype from Value......Page 33
Building the Data Science Team......Page 35
The Data Team......Page 36
The Startup......Page 37
Large Organizations......Page 38
Data Engineer......Page 40
Data Scientist......Page 43
Data Science Manager......Page 48
Where to Find the Data Team......Page 52
Interviewing for Data Science......Page 53
Onboarding the Data Science Team......Page 57
Managing the Team......Page 60
Embedded vs. Dedicated......Page 65
How Does Data Science Interact with Other Groups?......Page 68
Empowering Others to Use Data......Page 70
Interaction Difficulties......Page 74
Internal Difficulties......Page 77
Managing Data Analysis......Page 81
Epicycle of Analysis......Page 82
Types of Questions......Page 87
Exploratory Data Analysis......Page 91
What Are the Goals of Formal Modeling?......Page 97
Associational Analyses......Page 99
Prediction Analyses......Page 102
Interpretation......Page 106
Communication......Page 109
Data Science in Real Life......Page 112
What You've Gotten Yourself Into......Page 113
Data double duty......Page 115
Randomization versus observational studies......Page 116
The Data Pull is Clean......Page 118
The Experiment is Carefully Designed: Principles......Page 122
Causality......Page 123
Confounding......Page 128
A/B testing......Page 130
Sampling......Page 131
Blocking and Adjustment......Page 133
Multiple comparisons......Page 135
Effect sizes, significance, modeling......Page 137
Comparison with benchmark effects......Page 139
Negative controls......Page 140
The decision is (not) obvious......Page 143
Estimation target is relevant......Page 144
Analysis Product is Awesome......Page 147
About the Authors......Page 150

✦ Subjects

Data Science Enterprise