Hubbard - Java data analysis: data mining, big data analysis, NoSQL, and data visualization
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Get the most out of the popular Java libraries and tools to perform efficient data analysis
About This Book
- Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning.
- This is your companion to understanding and implementing a solid data analysis solution using Java
Who This Book Is For
If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required.
What You Will Learn
- Develop Java programs that analyze data sets of nearly any size, including text
- Implement...
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Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: September 2017
Production reference: 1130917
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78728-565-1
www.packtpub.com
Author
John R. Hubbard
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Erin Paciorkowski
Alexey Zinoviev
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John R. Hubbard has been doing computer-based data analysis for over 40 years at colleges and universities in Pennsylvania and Virginia. He holds an MSc in computer science from Penn State University and a PhD in mathematics from the University of Michigan. He is currently a professor of mathematics and computer science, Emeritus, at the University of Richmond, where he has been teaching data structures, database systems, numerical analysis, and big data.
Dr. Hubbard has published many books and research papers, including six other books on computing. Some of these books have been translated into German, French, Chinese, and five other languages. He is also an amateur timpanist.
I would like to thank the reviewers of this book for their valuable comments and suggestions. I would also like to thank the energetic team at Packt for publishing the book and helping me perfect it. Finally, I would like to thank my family for supporting me through everything.
Erin Paciorkowski studied computer science at the Georgia Institute of Technology as a National Merit Scholar. She has worked in Java development for the Department of Defense for over 8 years and is also a graduate teaching assistant for the Georgia Tech Online Masters of Computer Science program. She is a certified scrum master and holds Security+, Project+, and ITIL Foundation certifications. She was a Grace Hopper Celebration Scholar in 2016. Her interests include data analysis and information security.
Alexey Zinoviev is a lead engineer and Java and big data trainer at EPAM Systems, with a focus on Apache Spark, Apache Kafka, Java concurrency, and JVM internals. He has deep expertise in machine learning, large graph processing, and the development of distributed scalable Java applications. You can follow him at @zaleslaw or https://github.com/zaleslaw.
Currently, he's working on a Spark Tutorial at https://github.com/zaleslaw/Spark-Tutorial and on an Open GitBook about Spark (in Russian) at https://zaleslaw.gitbooks.io/data-processing-book/content/.
Thanks to my wife, Anastasya, and my little son, Roman, for quietly tolerating the very long hours I've been putting into this book.
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"It has been said that you don't really understand something until you have taught it to someone else. The truth is that you don't really understand it until you have taught it to a computer; that is, implemented it as an algorithm." |
| -- Donald Knuth |
As Don Knuth so wisely said, the best way to understand something is to implement it. This book will help you understand some of the most important algorithms in data science by showing you how to implement them in the Java programming language.
The algorithms and data management techniques presented here are often categorized under the general fields of data science, data analytics, predictive analytics, artificial intelligence, business intelligence, knowledge discovery, machine learning, data mining, and big data. We have included many that are relatively new, surprisingly powerful, and quite exciting. For example, the ID3 classification algorithm, the K-means and K-medoid clustering algorithms, Amazon's recommender system, and Google's PageRank algorithm have become ubiquitous in their effect on nearly everyone who uses electronic devices on the web.
We chose the Java programming language because it is the most widely used language and because of the reasons that make it so: it is available, free, everywhere; it is object-oriented; it has excellent support systems, such as powerful integrated development environments; its documentation system is efficient and very easy to use; and there is a multitude of open source libraries from third parties that support essentially all implementations that a data analyst is likely to use. It's no coincidence that systems such as MongoDB, which we study in ,
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