Data Science

1 Introduction 1

1.1 Why Managers Need to Know About Data Science 1

1.2 The New Age of Data Literacy 2

1.3 Data-Driven Development 3

1.4 How to Use this Book 4

2 The Business Side of Data Science 7

2.1 What Is Data Science? 7

2.2 Data Science in an Organization 19

2.3 Hiring Data Scientists 25

2.4 Management Failure Cases 36

3 Working with Modern Data 41

3.1 Unstructured Data and Passive Collection 41

3.2 Data Types and Sources 42

3.3 Data Formats 43

3.4 Databases 47

3.5 Data Analytics Software Architectures 50

4 Telling the Story, Summarizing Data 55

4.1 Choosing What to Measure 56

4.2 Outliers, Visualizations, and the Limits of Summary Statistics: A Picture IsWorth a Thousand Numbers 58

4.3 Experiments, Correlation, and Causality 60

4.4 Summarizing One Number 62

4.5 Key Properties to Assess: Central Tendency, Spread, and Heavy Tails 63

4.6 Summarizing Two Numbers: Correlations and Scatterplots 68

4.7 Advanced Material: Fitting a Line or Curve 72

4.8 Statistics: How to Not Fool Yourself 77

4.9 Advanced Material: Probability Distributions Worth Knowing 86

5 Machine Learning 101

5.1 Supervised Learning, Unsupervised Learning, and Binary Classifiers 102

5.2 Measuring Performance 107

5.3 Advanced Material: Important Classifiers 113

5.4 Structure of the Data: Unsupervised Learning 124

5.5 Learning as You Go: Reinforcement Learning 135

6 Knowing the Tools 141

6.1 A Note on Learning to Code 141

6.2 Cheat Sheet 142

6.3 Parts of the Data Science Ecosystem 143

6.4 Advanced Material: Database Query Crash Course 153

7 Deep Learning and Artificial Intelligence 161

7.1 Overview of AI 161

7.2 Neural Networks 164

7.3 Natural Language Processing 172

7.4 Knowledge Bases and Graphs 177

Postscript 181

Index 183