Becoming a Data Head - Alex J Gutman

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Résumé :

This book is for anyone, or any organization, asking how to bring a data mindset to the whole company, not just those trained in the space.
-Eric Weber, Head of Experimentation & Metrics Research at Yelp

A concise and readable guide to data and data science

In Becoming a Data Head, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.

Becoming a Data Head will show you how to:

• Think statistically and understand the role variation plays in your life and decision making.
• Become data literate-speak intelligently and ask the right questions about the statistics and results you encounter in the workplace.
• Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence.
• Avoid common pitfalls when working with and interpreting data.

Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head-an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you....

Biographie:

Acknowledgments xiii

Foreword xxiii

Introduction xxvii

Part One Thinking Like a Data Head

Chapter 1 What Is the Problem? 3

Questions a Data Head Should Ask 4

Why Is This Problem Important? 4

Who Does This Problem Affect? 6

What If We Don't Have the Right Data? 6

When Is the Project Over? 7

What If We Don't Like the Results? 7

Understanding Why Data Projects Fail 8

Customer Perception 8

Discussion 10

Working on Problems That Matter 11

Chapter Summary 11

Chapter 2 What Is Data? 13

Data vs. Information 13

An Example Dataset 14

Data Types 15

How Data Is Collected and Structured 16

Observational vs. Experimental Data 16

Structured vs. Unstructured Data 17

Basic Summary Statistics 18

Chapter Summary 19

Chapter 3 Prepare to Think Statistically 21

Ask Questions 22

There Is Variation in All Things 23

Scenario: Customer Perception (The Sequel) 24

Case Study: Kidney-Cancer Rates 26

Probabilities and Statistics 28

Probability vs. Intuition 29

Discovery with Statistics 31

Chapter Summary 33

Part Two Speaking Like a Data Head

Chapter 4 Argue with the Data 37

What Would You Do? 38

Missing Data Disaster 39

Tell Me the Data Origin Story 43

Who Collected the Data? 44

How Was the Data Collected? 44

Is the Data Representative? 45

Is There Sampling Bias? 46

What Did You Do with Outliers? 46

What Data Am I Not Seeing? 47

How Did You Deal with Missing Values? 47

Can the Data Measure What You Want It to Measure? 48

Argue with Data of All Sizes 48

Chapter Summary 49

Chapter 5 Explore the Data 51

Exploratory Data Analysis and You 52

Embracing the Exploratory Mindset 52

Questions to Guide You 53

The Setup 53

Can the Data Answer the Question? 54

Set Expectations and Use Common Sense 54

Do the Values Make Intuitive Sense? 54

Watch Out: Outliers and Missing Values 58

Did You Discover Any Relationships? 59

Understanding Correlation 59

Watch Out: Misinterpreting Correlation 60

Watch Out: Correlation Does Not Imply Causation 62

Did You Find New Opportunities in the Data? 63

Chapter Summary 63

Chapter 6 Examine the Probabilities 65

Take a Guess 66

The Rules of the Game 66

Notation 67

Conditional Probability and Independent Events 69

The Probability of Multiple Events 69

Two Things That Happen Together 69

One Thing or the Other 70

Probability Thought Exercise 72

Next Steps 73

Be Careful Assuming Independence 74

Don't Fall for the Gambler's Fallacy 74

All Probabilities Are Conditional 75

Don't Swap Dependencies 76

Bayes' Theorem 76

Ensure the Probabilities Have Meaning 79

Calibration 80

Rare Events Can, and Do, Happen 80

Chapter Summary 81

Chapter 7 Challenge the Statistics 83

Quick Lessons on Inference 83

Give Yourself Some Wiggle Room 84

More Data, More Evidence 84

Challenge the Status Quo 85

Evidence to the Contrary 86

Balance Decision Errors 88

The Process of Statistical Inference 89

The Questions You Should Ask to Challenge the Statistics 90

What Is the Context for These Statistics? 90

What Is the Sample Size? 91

What Are You Testing? 92

What Is the Null Hypothesis? 92

Assuming Equivalence 93

What Is the Significance Level? 93