Description
Book SynopsisTable of ContentsIntroduction 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
