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John Paul Mueller - Python for Data Science For Dummies

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John Paul Mueller Python for Data Science For Dummies

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Part 1: Getting started with data science and Python -- Part 2: Getting your hands dirty with data -- Part 3: Visualizing information -- Part 4: Wrangling data -- Part 5: Learning from data -- Part 6: The part of tens.

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Python for Data Science For Dummies, 2nd Edition

Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com

Copyright 2019 by John Wiley & Sons, Inc., Hoboken, New Jersey

Media and software compilation copyright 2019 by John Wiley & Sons, Inc. All rights reserved.

Published simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions .

Trademarks: Wiley, For Dummies, the Dummies Man logo, Dummies.com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and may not be used without written permission. Python is a registered trademark of Python Software Foundation Corporation. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

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Library of Congress Control Number: 2018967877

ISBN: 978-1-119-54762-4; ISBN: 978-1-119-54766-2 (ebk); ISBN: 978-1-119-54764-8 (ebk)

Python for Data Science For Dummies
To view this book's Cheat Sheet, simply go to www.dummies.com and search for Python for Data Science For Dummies Cheat Sheet in the Search box.
Table of Contents
List of Tables
  1. Chapter 8
  2. Chapter 10
  3. Chapter 18
  4. Chapter 19
List of Illustrations
  1. Chapter 1
  2. Chapter 2
  3. Chapter 3
  4. Chapter 4
  5. Chapter 5
  6. Chapter 6
  7. Chapter 8
  8. Chapter 10
  9. Chapter 11
  10. Chapter 13
  11. Chapter 14
  12. Chapter 15
  13. Chapter 16
  14. Chapter 18
  15. Chapter 19
  16. Chapter 20
Guide
Pages
Introduction

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. Its 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, 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, 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 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 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. Its 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 youre one of them, you can use this book to get a leg up on the process.

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