Julia for Data Science PDF
Preview Julia for Data Science
Published by: 2 Lindsley Road Basking Ridge, NJ 07920 USA https://www.TechnicsPub.com Cover design by John Fiorentino Edited by Lauren McCafferty All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the publisher, except for the inclusion of brief quotations in a review. The author and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. All trade and product names are trademarks, registered trademarks, or service marks of their respective companies, and are the property of their respective holders and should be treated as such. Copyright © 2016 by Technics Publications, LLC ISBN, print ed. 9781634621304 ISBN, ePub ed. 9781634621328 First Printing 2016 Library of Congress Control Number: 2016944030 Dedicated to the memory of my father, Nicholas C. Voulgaris Contents at a Glance Introduction CHAPTER 1: Introducing Julia CHAPTER 2: Setting Up the Data Science Lab CHAPTER 3: Learning the Ropes of Julia CHAPTER 4: Going Beyond the Basics in Julia CHAPTER 5: Julia Goes All Data Science-y CHAPTER 6: Julia the Data Engineer CHAPTER 7: Exploring Datasets CHAPTER 8: Manipulating the Fabric of the Data Space CHAPTER 9: Sampling Data and Evaluating Results CHAPTER 10: Unsupervised Machine Learning CHAPTER 11: Supervised Machine Learning CHAPTER 12: Graph Analysis CHAPTER 13: Reaching the Next Level APPENDIX A: Downloading and Installing Julia and IJulia APPENDIX B: Useful Websites Related to Julia APPENDIX C: Packages Used in This Book APPENDIX D: Bridging Julia with Other Platforms APPENDIX E: Parallelization in Julia APPENDIX F: Answers to Chapter Challenges Index Introduction Idiscovered Julia a few years back, and I’ve been intrigued by its potential and its power ever since. Julia’s user-friendly Integrated Development Environment (IDE) made it very accessible, and its high level logic (very similar to Matlab and other high level languages) and performance made it powerful. However, I was more involved in other, more established platforms such as R and Java, and unable to give Julia my full attention. As such, I didn’t delve much further beyond the basics, including the applications of the tutorial that was available at the time. Besides, I knew that there are constantly new and interesting languages being developed, most of which never become mainstream. So, why am I bothering with Julia now? Well, for one, it remained relevant as the years went by; the number of attendees at the Julia conference has been growing considerably every year. Even though I had been familiar with its basics, when I revisited Julia I discovered that I still had a lot to learn, as it had evolved considerably since I’d first encountered it. Most importantly, it had crossed the pond and made its presence known to people in Europe, one of whom had created a series of exercises and videos about this fairly new language. After playing around with Version 0.2, I began to wonder if I could actually do something useful with it, beyond factoring numbers quickly or calculating the nth Fibonacci number. However, the few packages that were available with Version 0.2 were poorly documented. There were only a handful of videos introducing the language, most of which were talks from a Python conference. Still, I kept Julia installed on my machine and
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