Java for Data Science (Paperback or Softback)

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.

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.

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.