Data is increasingly used for every possible purpose, and many of those purposes elude attention, but every time you get on the Internet, you generate even more. It’s not just you, either; the growth of the Internet has been phenomenal, according to Internet World Stats (https://www.internetworldstats.com/emarketing.htm). Data science turns this huge amount of data into something useful — something that you use absolutely every day to perform an amazing array of tasks or to obtain services from someone else.

In fact, you’ve probably used data science in ways that you never expected. For example, when you used your favorite search engine this morning to look for something, it made suggestions on alternative search terms. Those terms are supplied by data science. When you went to the doctor last week and discovered the lump you found wasn’t cancer, the doctor likely made her prognosis with the help of data science. In fact, you might work with data science every day and not even know it. Python for Data Science For Dummies, 2nd Edition not only gets you started using data science to perform a wealth of practical tasks but also helps you realize just how many places data science is used. By knowing how to answer data science problems and where to employ data science, you gain a significant advantage over everyone else, increasing your chances at promotion or that new job you really want.

About This Book

The main purpose of Python for Data Science For Dummies, 2nd 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 might 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 Anaconda, a product that includes IPython and Jupyter Notebook — two tools that take the sting out of working with Python. You experiment with IPython in a fully interactive environment. The code you place in Jupyter Notebook (also called just Notebook throughout the book) is presentation quality, and you can mix a number of presentation elements right there in your document. It’s not really like using a development environment at all. To make this book easier to use on alternative platforms, you also discover an online Interactive Development Environment application (IDE) named Google Colab that allows you to interact with most, but not quite all, of the book examples using your favorite tablet or (assuming that you can squint well enough) your smart phone.

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 Scikit-learn to perform some really 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 might 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 installation instructions for Anaconda, which are followed by the 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 second 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. Consequently, you get a lot more out of this edition of the book as a result of the input provided by hundreds of readers before you.

To make absorbing the concepts even easier, this book uses the following conventions:

• Text that you’re meant to type just as it appears in the book is in bold. The exception is when you’re working through a step list: Because each step is bold, the text to type is not bold.
• When you see words in italics as part of a typing sequence, you need to replace that value with something that works for you. For example, if you see “Type Your Name and press Enter,” you need to replace Your Name with your actual name.
• Web addresses and programming code appear in monofont. If you’re reading a digital version of this book on a device connected to the Internet, note that you can click the web address to visit that website, like this: http://www.dummies.com.
• When you need to type command sequences, you see them separated by a special arrow, like this: File ⇒ New File. In this example, you go to the File menu first and then select the New File entry on that menu.

Foolish Assumptions

You might find it difficult to believe that we’ve assumed anything about you — after all, we haven’t even met you yet! Although most assumptions are indeed foolish, we made these assumptions to provide a starting point for the book.

You need to e familiar with the platform you want to use because the book doesn’t offer any guidance in this regard. (Chapter 3 does, however, provide Anaconda installation instructions, and Chapter 4 gets you started with Google Colab.) To provide you with maximum information about Python concerning how it applies to data science, this book doesn’t discuss any platform-specific issues. You really do need to know how to install applications, use applications, and generally work with your chosen platform before you begin working with this book.

You must know how to work with Python. This edition of the book no longer contains a Python primer because you can find such as wealth of tutorials online (see https://www.w3schools.com/python/ and https://www.tutorialspoint.com/python/ as examples).

This book isn’t a math primer. Yes, you see lots of examples of complex math, but the emphasis is on helping you use Python and data science to perform analysis tasks rather than teaching math theory. Chapters 1 and 2 give you a better understanding of precisely what you need to know to use this book successfully.

This book also assumes that you can access items on the Internet. Sprinkled throughout are numerous references to online material that will enhance your learning experience. However, these added sources are useful only if you actually find and use them.

Beyond the Book

This book isn’t the end of your Python or data science experience — it’s really just the beginning. We provide online content to make this book more flexible and better able to meet your needs. That way, as we receive e-mail from you, we can address questions and tell you how updates to either Python or its associated add-ons affect book content. In fact, you gain access to all these cool additions:

• Cheat sheet: You remember using crib notes in school to make a better mark on a test, don’t you? You do? Well, a cheat sheet is sort of like that. It provides you with some special notes about tasks that you can do with Python, IPython, IPython Notebook, and data science that not every other person knows. You can find the cheat sheet by going to www.dummies.com, searching this book’s title, and scrolling down the page that appears. The cheat sheet contains really neat information such as the most common programming mistakes that cause people woe when using Python.

• Updates: Sometimes changes happen. For example, we might not have seen an upcoming change when we looked into our crystal ball during the writing of this book. In the past, this possibility simply meant that the book became outdated and less useful, but you can now find updates to the book by searching this book’s title at www.dummies.com.

  In addition to these updates, check out the blog posts with answers to reader questions and demonstrations of useful book-related techniques at http://blog.johnmuellerbooks.com/.

• Companion files: Hey! Who really wants to type all the code in the book and reconstruct all those plots manually? Most readers would prefer to spend their time actually working with Python, performing data science tasks, and seeing the interesting things they can do, rather than typing. Fortunately for you, the examples used in the book are available for download, so all you need to do is read the book to learn Python for data science usage techniques. You can find these files at www.dummies.com. Search this book’s title, and on the page that appears, scroll down to the image of the book cover and click it. Then click the More about This Book button and on the page that opens, go to the Downloads tab.

Where to Go from Here

It’s time to start your Python for data science adventure! If you’re completely new to Python and its use for data science tasks, you should start with Chapter 1 and progress through the book at a pace that allows you to absorb as much of the material as possible.

If you’re a novice who’s in an absolute rush to get going with Python for data science as quickly as possible, you can skip to Chapter 3 with the understanding that you may find some topics a bit confusing later. Skipping to Chapter 5 is okay if you already have Anaconda (the programming product used in the book) installed, but be sure to at least skim Chapter 3 so that you know what assumptions we made when writing this book. If you plan to use your tablet to work with this book, be certain to review Chapter 4 so that you understand the limitations presented by Google Colab in running the example code; not all of the examples work in this IDE. Make sure to install Anaconda with Python version 3.6.5 installed to obtain the best results from the book’s source code.

Readers who have some exposure to Python and have Anaconda installed can save reading time by moving directly to Chapter 5. You can always go back to earlier chapters as necessary when you have questions. However, you should understand how each technique works before moving to the next one. Every technique, coding example, and procedure has important lessons for you, and you could miss vital content if you start skipping too much information.

IN THIS PART …

Understanding how Python can make data science easier.

Defining the Python features commonly used for data science.

Creating a Python setup of your own.

Working with Google Colab on alternative devices.