The Data Science Handbook - Field Cady

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Résumé :

Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields.

Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are:

• An emphasis on software engineering and coding skills, which play a significant role in most real data science problems.
• Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms).
• A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more.
• Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization.
• Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool's limitations, and reasoning about discoveries.

Data science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization....

Biographie:

Field Cady is a data scientist, researcher and author based in Seattle, WA, USA. He has worked for a range of companies including Google, the Allen Institute for Artificial Intelligence, and several startups. He received a BS in physics and math from Stanford and did graduate work computer science at Carnegie Mellon. He is the author of The Data Science Handbook (Wiley 2017)....

Sommaire:

Preface to the First Edition xvii

Preface to the Second Edition xix

1 Introduction 1

1.1 What Data Science Is and Isn't 2

1.2 This Book's Slogan: Simple Models Are Easier to Work With 3

1.3 How Is This Book Organized? 4

1.4 How to Use This Book? 4

1.5 Why Is It All in Python, Anyway? 4

1.6 Example Code and Datasets 5

1.7 Parting Words 5

Part I The Stuff You'll Always Use 7

2 The Data Science Road Map 9

2.1 Frame the Problem 10

2.2 Understand the Data: Basic Questions 11

2.3 Understand the Data: Data Wrangling 12

2.4 Understand the Data: Exploratory Analysis 12

2.5 Extract Features 13

2.6 Model 14

2.7 Present Results 14

2.8 Deploy Code 14

2.9 Iterating 15

2.10 Glossary 15

3 Programming Languages 17

3.1 Why Use a Programming Language? What Are the Other Options? 17

3.2 A Survey of Programming Languages for Data Science 18

3.3 Where to Write Code 20

3.4 Python Overview and Example Scripts 21

3.5 Python Data Types 25

3.6 GOTCHA: Hashable and Unhashable Types 30

3.7 Functions and Control Structures 31

3.8 Other Parts of Python 33

3.9 Python's Technical Libraries 35

3.10 Other Python Resources 39

3.11 Further Reading 39

3.12 Glossary 40

3a Interlude: My Personal Toolkit 41

4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning 43

4.1 The Worst Dataset in the World 43

4.2 How to Identify Pathologies 44

4.3 Problems with Data Content 44

4.4 Formatting Issues 46

4.5 Example Formatting Script 49

4.6 Regular Expressions 50

4.7 Life in the Trenches 53

4.8 Glossary 54

5 Visualizations and Simple Metrics 55

5.1 A Note on Python's Visualization Tools 56

5.2 Example Code 56

5.3 Pie Charts 56

5.4 Bar Charts 58

5.5 Histograms 59

5.6 Means, Standard Deviations, Medians, and Quantiles 61

5.7 Boxplots 62

5.8 Scatterplots 64

5.9 Scatterplots with Logarithmic Axes 65

5.10 Scatter Matrices 67

5.11 Heatmaps 68

5.12 Correlations 69

5.13 Anscombe's Quartet and the Limits of Numbers 71

5.14 Time Series 72

5.15 Further Reading 75

5.16 Glossary 75

6 Overview: Machine Learning and Artificial Intelligence 77

6.1 Historical Context 77

6.2 The Central Paradigm: Learning a Function from Example 78

6.3 Machine Learning Data: Vectors and Feature Extraction 79

6.4 Supervised, Unsupervised, and In-Between 79

6.5 Training Data, Testing Data, and the Great Boogeyman of Overfitting 80

6.6 Reinforcement Learning 81

6.7 ML Models as Building Blocks for AI Systems 82

6.8 ML Engineering as a New Job Role 82

6.9 Further Reading 83

6.10 Glossary 83

7 Interlude: Feature Extraction Ideas 85

7.1 Standard Features 85

7.2 Features that Involve Grouping 86

7.3 Preview of More Sophisticated Features 86

7.4 You Get What You Measure: Defining the Target Variable 87

8 Machine-Learning Classification 89

8.1 What Is a Classifier, and What Can You Do with It? 89

8.2 A Few Practical Concerns 90

8.3 Binary Versus Multiclass 90

8.4 Example Script 91

8.5 Specific Classifiers 92

8.6 Evaluating Classifiers 102

8.7 Selecting Classification Cutoffs 105

8.8 Further Reading 106

8.9 Glossary 106

9 Technical Communication and Documentation 109

9.1 Several Guiding Principles 109

9.2 Slide Deck...