Data science with Java: practical methods for scientists and engineers PDF
Preview Data science with Java: practical methods for scientists and engineers
Data Science with Java Michael R. Brzustowicz, PhD Data Science with Java by Michael R. Brzustowicz, PhD Copyright © 2017 Michael Brzustowicz. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly 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 [email protected]. Editors: Nan Barber and Brian Foster Production Editor: Kristen Brown Copyeditor: Sharon Wilkey Proofreader: Jasmine Kwityn Indexer: Lucie Haskins Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest June 2017: First Edition Revision History for the First Edition 2017-05-30: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Science with Java, the cover image, and related trade dress are trademarks of O’Reilly 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 was downloaded from AvaxHome! Visit my blog for more new books: https://avxhm.se/blogs/AlenMiler Dedication This book is for my cofounder and our two startups. Preface 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. Who Should Read This Book 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. Why I Wrote This Book 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 engineering–science 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. A Word on Data Science Today 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.
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