Table of Contents

Introduction 1

Part 1: Getting Started with Data Science and Python 7

Chapter 1: Discovering the Match between Data Science and Python 9

Chapter 2: Introducing Python’s Capabilities and Wonders 21

Chapter 3: Setting Up Python for Data Science 39

Chapter 4: Working with Google Colab 59

Part 2: Getting Your Hands Dirty with Data 81

Chapter 5: Understanding the Tools 83

Chapter 6: Working with Real Data 99

Chapter 7: Conditioning Your Data 121

Chapter 8: Shaping Data 149

Chapter 9: Putting What You Know in Action 169

Part 3: Visualizing Information 183

Chapter 10: Getting a Crash Course in MatPlotLib 185

Chapter 11: Visualizing the Data 201

Part 4: Wrangling Data 227

Chapter 12: Stretching Python’s Capabilities 229

Chapter 13: Exploring Data Analysis 251

Chapter 14: Reducing Dimensionality 275

Chapter 15: Clustering 295

Chapter 16: Detecting Outliers in Data 313

Part 5: Learning from Data 327

Chapter 17: Exploring Four Simple and Effective Algorithms 329

Chapter 18: Performing Cross-Validation, Selection, and Optimization 347

Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 371

Chapter 20: Understanding the Power of the Many 411

Part 6: The Part of Tens 429

Chapter 21: Ten Essential Data Resources 431

Chapter 22: Ten Data Challenges You Should Take 437

Index 447

About the Author

John Paul Mueller is a tech editor and the author of over 100 books on topics from networking and home security to database management and heads-down programming. Follow John's blog at http://blog.johnmuellerbooks.com/. Luca Massaron is a data scientist who specializes in organizing and interpreting big data and transforming it into smart data. He is a Google Developer Expert (GDE) in machine learning.