Details

<p>Introduction 1</p> <p><b>Part 1: Getting Started with Data Science and Python 7</b></p> <p>Chapter 1: Discovering the Match between Data Science and Python 9</p> <p>Chapter 2: Introducing Python’s Capabilities and Wonders 21</p> <p>Chapter 3: Setting Up Python for Data Science 33</p> <p>Chapter 4: Working with Google Colab 49</p> <p><b>Part 2: Getting Your Hands Dirty with Data 71</b></p> <p>Chapter 5: Working with Jupyter Notebook 73</p> <p>Chapter 6: Working with Real Data 83</p> <p>Chapter 7: Processing Your Data 105</p> <p>Chapter 8: Reshaping Data 131</p> <p>Chapter 9: Putting What You Know into Action 143</p> <p><b>Part 3: Visualizing Information 157</b></p> <p>Chapter 10: Getting a Crash Course in Matplotlib 159</p> <p>Chapter 11: Visualizing the Data 177</p> <p><b>Part 4: Wrangling Data 199</b></p> <p>Chapter 12: Stretching Python’s Capabilities 201</p> <p>Chapter 13: Exploring Data Analysis 223</p> <p>Chapter 14: Reducing Dimensionality 251</p> <p>Chapter 15: Clustering 273</p> <p>Chapter 16: Detecting Outliers in Data 291</p> <p><b>Part 5: Learning from Data 305</b></p> <p>Chapter 17: Exploring Four Simple and Effective Algorithms 307</p> <p>Chapter 18: Performing Cross-Validation, Selection, and Optimization 327</p> <p>Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 351</p> <p>Chapter 20: Understanding the Power of the Many 391</p> <p><b>Part 6: The Part of Tens 413</b></p> <p>Chapter 21: Ten Essential Data Resources 415</p> <p>Chapter 22: Ten Data Challenges You Should Take 421</p> <p>Index 431</p>

<p><b>John Paul Mueller</b> is a freelance author and technical editor who has written 124 books on topics ranging like networking, home security, database management, and heads-down programming. <b>Luca Massaron</b> is a data scientist specialized in solving real-world problems with AI, machine learning, and algorithms. He is also a Kaggle Grandmaster and a Google Developer Expert.

<p><b>Untangle data science with Python^®</b> <p>With the Python programming language, you can write code to analyze, visualize, and model datasets of any size. This book simplifies this complex task. Even if you’re a beginner, you can learn to set up a Python workspace, shuffle and shape your data, and create valuable charts and graphs. When you’re ready, move on to more advanced transformations and analyses that will let you pull new ideas and insights right out of your data cloud. There’s no complicated jargon in this book—just clear instructions on taming data with Python. <p><b>Inside… <ul><li>Conquering Python & data science basics</li> <li>Setting up a workspace with Anaconda^® and Google^® Colab</li> <li>Importing and cleaning data</li> <li>Working with neural networks and ensembles of decision trees</li> <li>Ensuring data security</li> <li>Working with updated datasets</b></li></ul>

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