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 33 Chapter 4: Working with Google Colab 49 Part 2: Getting Your Hands Dirty with Data 71 Chapter 5: Working with Jupyter Notebook 73 Chapter 6: Working with Real Data 83 Chapter 7: Processing Your Data 105 Chapter 8: Reshaping Data 131 Chapter 9: Putting What You Know into Action 143 Part 3: Visualizing Information 157 Chapter 10: Getting a Crash Course in Matplotlib 159 Chapter 11: Visualizing the Data 177 Part 4: Wrangling Data 199 Chapter 12: Stretching Python's Capabilities 201 Chapter 13: Exploring Data Analysis 223 Chapter 14: Reducing Dimensionality 251 Chapter 15: Clustering 273 Chapter 16: Detecting Outliers in Data 291 Part 5: Learning from Data 305 Chapter 17: Exploring Four Simple and Effective Algorithms 307 Chapter 18: Performing Cross-Validation, Selection, and Optimization 327 Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 351 Chapter 20: Understanding the Power of the Many 391 Part 6: The Part of Tens 413 Chapter 21: Ten Essential Data Resources 415 Chapter 22: Ten Data Challenges You Should Take 421 Index 431.