Becoming a Data Head How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."
Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage

You've heard the hype around data—now get the facts.

In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, 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.

You'll learn how to:

• Think statistically and understand the role variation plays in your life and decision making
• 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.

ALEX J. GUTMAN, PhD, is a Data Scientist, Corporate Trainer, and Accredited Professional Statistician. His professional focus is on statistical and machine learning and he has extensive experience working as a Data Scientist for the Department of Defense and two Fortune 50 companies.

JORDAN GOLDMEIER is a Data Scientist, author, speaker, and community leader. He is a seven-time recipient of the Microsoft Most Valuable Professional Award and he has taught analytics to members of the Pentagon and Fortune 500 companies.

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

How Many Tests Are You Doing? 94

Can I See the Confidence Intervals? 95

Is This Practically Significant? 96

Are You Assuming Causality? 96

Chapter Summary 97

Part Three Understanding the Data Scientist’s Toolbox

Chapter 8 Search for Hidden Groups 101

Unsupervised Learning 102

Dimensionality Reduction 102

Creating Composite Features 103

Principal Component Analysis 105

Principal Components in Athletic Ability 105

PCA Summary 108

Potential Traps 109

Clustering 110

k-Means Clustering 111

Clustering Retail Locations 111

Potential Traps 113

Chapter Summary 114

Chapter 9 Understand the Regression Model 117

Supervised Learning 117

Linear Regression: What It Does 119

Least Squares Regression: Not Just a Clever Name 120

Linear Regression: What It Gives You 123

Extending to Many Features 124

Linear Regression: What Confusion It Causes 125

Omitted Variables 125

Multicollinearity 126

Data Leakage 127

Extrapolation Failures 128

Many Relationships Aren’t Linear 128

Are You Explaining or Predicting? 128

Regression Performance 130

Other Regression Models 131

Chapter Summary 131

Chapter 10 Understand the Classification Model 133

Introduction to Classification 133

What You’ll Learn 134

Classification Problem Setup 135

Logistic Regression 135

Logistic Regression: So What? 138

Decision Trees 139

Ensemble Methods 142

Random Forests 143

Gradient Boosted Trees 143

Interpretability of Ensemble Models 145

Watch Out for Pitfalls 145

Misapplication of the Problem 146

Data Leakage 146

Not Splitting Your Data 146

Choosing the Right Decision Threshold 147

Misunderstanding Accuracy 147

Confusion Matrices 148

Chapter Summary 150

Chapter 11 Understand Text Analytics 151

Expectations of Text Analytics 151

How Text Becomes Numbers 153

A Big Bag of Words 153

N-Grams 157

Word Embeddings 158

Topic Modeling 160

Text Classification 163

Naïve Bayes 164

Sentiment Analysis 166

Practical Considerations When Working with Text 167

Big Tech Has the Upper Hand 168

Chapter Summary 169

Chapter 12 Conceptualize Deep Learning 171

Neural Networks 172

How Are Neural Networks Like the Brain? 172

A Simple Neural Network 173

How a Neural Network Learns 174

A Slightly More Complex Neural Network 175

Applications of Deep Learning 178

The Benefits of Deep Learning 179

How Computers “See” Images 180

Convolutional Neural Networks 182

Deep Learning on Language and Sequences 183

Deep Learning in Practice 185

Do You Have Data? 185

Is Your Data Structured? 186

What Will the Network Look Like? 186

Artificial Intelligence and You 187

Big Tech Has the Upper Hand 188

Ethics in Deep Learning 189

Chapter Summary 190

Part Four Ensuring Success

Chapter 13 Watch Out for Pitfalls 193

Biases and Weird Phenomena in Data 194

Survivorship Bias 194

Regression to the Mean 195

Simpson’s Paradox 195

Confirmation Bias 197

Effort Bias (aka the “Sunk Cost Fallacy”) 197

Algorithmic Bias 198

Uncategorized Bias 198

The Big List of Pitfalls 199

Statistical and Machine Learning Pitfalls 199

Project Pitfalls 200

Chapter Summary 202

Chapter 14 Know the People and Personalities 203

Seven Scenes of Communication Breakdowns 204

The Postmortem 204

Storytime 205

The Telephone Game 206

Into the Weeds 206

The Reality Check 207

The Takeover 207

The Blowhard 208

Data Personalities 208

Data Enthusiasts 209

Data Cynics 209

Data Heads 209

Chapter Summary 210

Chapter 15 What’s Next? 211

Index 215

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