Luca Massaron - Python for data science [for dummies]

1. Table of Contents

You rely on data science absolutely every day to perform an amazing array of tasks or to obtain services from someone else. In fact, youve 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 wasnt cancer, its likely the doctor made his 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 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.

The main purpose of Python for Data Science For Dummies 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 thats far from the truth. Python comes with a host of useful libraries that do all the heavy lifting for you in the background. You dont even realize how much is going on, and you dont need to care. All you really need to know is that you want to perform specific tasks and that 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 IPython 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 IPython Notebook is presentation quality, and you can mix a number of presentation elements right there in your document. Its not really like using a 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, for which this book provides you with all the details. This book also spends considerable time showing you just what is available and how you can use it to perform some really interesting calculations. Many people would like to know how to perform handwriting recognition and if youre 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 doesnt leave you in the dark there, either. At the beginning, you find complete installation instructions for Anaconda and a quick primer (with references) to the basic Python programming you need to perform. The emphasis is on getting you up and running as quickly as possible, and to make examples straightforward and simple so that the code doesnt become a stumbling block to learning.

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

• Text that youre meant to type just as it appears in the book is in bold. The exception is when youre 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.