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The main purpose of Python for Data Science For Dummies, 3rd Edition, is to take the scare factor out of data science by showing you that data science is not only really interesting but also quite doable using Python. You may assume that you need to be a computer science genius to perform the complex tasks normally associated with data science, but that’s far from the truth. Python comes with a host of useful libraries that do all the heavy lifting for you in the background. You don’t even realize how much is going on, and you don’t need to care. All you really need to know is that you want to perform specific tasks, and Python makes these tasks quite accessible. Part of the emphasis of this book is on using the right tools. You start with either Jupyter Notebook (on desktop systems) or Google Colab (on the web) — two tools that take the sting out of working with Python. The code you place in Jupyter Notebook or Google Colab is presentation quality, and you can mix a number of presentation elements right there in your document. It’s not really like using a traditional development environment at all. You also discover some interesting techniques in this book. For example, you can create plots of all your data science experiments using Matplotlib, and this book gives you all the details for doing that. This book also spends considerable time showing you available resources (such as packages) and how you can use Scikitlearn to perform some very interesting calculations. Many people would like to know how to perform handwriting recognition, and if you’re one of them, you can use this book to get a leg up on the process. Of course, you may still be worried about the whole programming environment issue, and this book doesn’t leave you in the dark there, either. At the beginning, you find complete methods you need to get started with data science using Jupyter Notebook or Google Colab. The emphasis is on getting you up and running as quickly as possible, and to make examples straightforward and simple so that the code doesn’t become a stumbling block to learning. This third edition of the book provides you with updated examples using Python 3.x so that you’re using the most modern version of Python while reading. In addition, you find a stronger emphasis on making examples simpler, but also making the environment more inclusive by adding material on deep learning. More important, this edition of the book contains updated datasets that better demonstrate how data science works today. This edition of the book also touches on modern concerns, such as removing personally identifiable information and enhancing data security. Consequently, you get a lot more out of this edition of the book as a result of the input provided by thousands of readers before you.