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