Java for Data Science

Richard M. Reese, Jennifer L. Reese

• Editorial: Packt Publishing
• Año: 2017
• ISBN:
  9781785280115
• Edición: 1
• Categoría: Ciencia de la computación
• Categoría: Tratamiento de datos

Examine the techniques and Java tools supporting the growing field of data science

About This Book

• Your entry ticket to the world of data science with the stability and power of Java
• Explore, analyse, and visualize your data effectively using easy-to-follow examples
• Make your Java applications more capable using machine learning

Who This Book Is For

This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.

What You Will Learn

• Understand the nature and key concepts used in the field of data science
• Grasp how data is collected, cleaned, and processed
• Become comfortable with key data analysis techniques
• See specialized analysis techniques centered on machine learning
• Master the effective visualization of your data
• Work with the Java APIs and techniques used to perform data analysis

In Detail

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application.

The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.

The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book.

Style and approach

This book follows a tutorial approach, providing examples of each of the major concepts covered.

With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Java for Data Science

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Java for Data Science

Copyright © 2017 Packt Publishing

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First published: January 2017

Production reference: 1050117

Published by Packt Publishing Ltd.

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ISBN 978-1-78528-011-5

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Credits

Authors Richard M. Reese Jennifer L. Reese	Copy Editors Vikrant Phadkay Safis Editing
Reviewers Walter Molina Shilpi Saxena	Project Coordinator Nidhi Joshi
Commissioning Editor Veena Pagare	Proofreader Safis Editing
Acquisition Editor Tushar Gupta	Indexer Aishwarya Gangawane
Content Development Editor Aishwarya Pandere	Graphics Disha Haria
Technical Editor Suwarna Patil	Production Coordinator Nilesh Mohite

About the Authors

Richard M. Reese has worked in both industry and academics. For 17 years, he worked in the telephone and aerospace industries, serving in several capacities, including research and development, software development, supervision, and training. He currently teaches at Tarleton State University, where he has the opportunity to apply his years of industry experience to enhance his teaching.

Richard has written several Java books and a C Pointer book. He uses a concise and easy-to-follow approach to topics at hand. His Java books have addressed EJB 3.1, updates to Java 7 and 8, certification, jMonkeyEngine, natural language processing, functional programming, and networks.

Richard would like to thank his wife, Karla, for her continued support, and to the staff of Packt Publishing for their work in making this a better book.

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her research interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education.

She previously worked as a software engineer developing software for county- and district-level government offices throughout Texas. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.

I would like to thank Dad for his inspiration and guidance, Mom for her patience and perspective, and Jace for his support and always believing in me.

About the Reviewers

Walter Molina is a UI and UX developer from Villa Mercedes, San Luis, Argentina. His skills include, but are not limited to, HTML5, CSS3, and JavaScript. He uses these technologies at a Jedi/ninja level (along with a plethora of JavaScript libraries) in his daily work as a frontend developer at Tachuso, a creative content agency. He holds a bachelor's degree in computer science and is a member of the School of Engineering at local National University, where he teaches programming skills to second- and third-year students. His LinkedIn profile is https://ar.linkedin.com/in/waltermolina.

Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (IOT and cloud computing space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the big data space for the last 3 years; she also handles a high-performance and geographically distributed team of elite engineers.

Shilpi has more than 14 years (3 years in the big data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn various hats, such as developer, technical leader, product owner, tech manager, and so on, and has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in big data on Storm and Impala with autoscaling in AWS.

Shilpi has also authored Real-time Analytics with Storm and Cassandra ( https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra ) and Real time Big Data Analytics ( https://www.packtpub.com/big-data-and-business-intelligence/real-time-big-data-analytics ) with Packt Publishing.

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Preface

In this book, we examine Java-based approaches to the field of data science. Data science is a broad topic and includes such subtopics...