Michael R. Brzustowicz - Data Science with Java: Practical Methods for Scientists and Engineers
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Youll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, youll find code examples you can use in your applications.
Examine methods for obtaining, cleaning, and arranging data into its purest form
Understand the matrix structure that your data should take
Learn basic concepts for testing the origin and validity of data
Transform your data into stable and usable numerical values
Understand supervised and unsupervised learning algorithms, and methods for evaluating their success
Get up and running with MapReduce, using customized components suitable for data science algorithms
Michael R. Brzustowicz: author's other books
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Michael R. Brzustowicz, PhD
by Michael R. Brzustowicz, PhD
Copyright 2017 Michael Brzustowicz. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- June 2017: First Edition
- 2017-05-30: First Release
The OReilly logo is a registered trademark of OReilly Media, Inc. Data Science with Java, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-491-93411-1
[LSI]
This book is for my cofounder and our two startups.
Data science is a diverse and growing field encompassing many subfields of both mathematics and computer science. Statistics, linear algebra, databases, machine intelligence, and data visualization are just a few of the topics that merge together in the realm of a data scientist. Technology abounds and the tools to practice data science are evolving rapidly. This book focuses on core, fundamental principles backed by clear, object-oriented code in Java. And while this book will inspire you to get busy right away practicing the craft of data science, it is my hope that you will take the lead in building the next generation of data science technology.
This book is for scientists and engineers already familiar with the concepts of application development who want to jump headfirst into data science. The topics covered here will walk you through the data science pipeline, explaining mathematical theory and giving code examples along the way. This book is the perfect jumping-off point into much deeper waters.
I wrote this book to start a movement. As data science skyrockets to stardom, fueled by R and Python, very few practitioners venture into the world of Java. Clearly, the tools for data exploration lend themselves to the interpretive languages. But there is another realm of the engineeringscience hybrid where scale, robustness, and convenience must merge. Java is perhaps the one language that can do it all. If this book inspires you, I hope that you will contribute code to one of the many open source Java projects that support data science.
Data science is continually changing, not only in scope but also in those practicing it. Technology moves very fast, with top algorithms moving in and out of favor in a matter of years or even months. Long-time standardized practices are discarded for practical solutions. And the barrier to success is regularly hurdled by those in fields previously untouched by quantitative science. Already, data science is an undergraduate curriculum. There is only one way to be successful in the future: know the math, know the code, and know the subject matter.
This book is a logical journey through a data science pipeline. In .
The following typographical conventions are used in this book:
Constant widthConstant width boldConstant width italicThis element signifies a tip or suggestion.
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Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/oreillymedia/Data_Science_with_Java.
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.
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