(Ebook) Java for Data Science by Reese, Richard M., Reese, Jennifer L. ISBN 9781785280115, 1785280112

(Ebook) Java for Data Science by Reese, Richard M., Reese, Jennifer L. ISBN 9781785280115, 1785280112

Examine the techniques and Java tools that are supporting the growing field of data scienceAbout This Book* Your entry ticket to the world of data science with the stability and power of Java* Explore, analyze, and visualize your data effectively using easy-to-follow examples* Make your Java applications smarter using machine learningWho This Book Is ForThis book is for Java developers who are comfortable with 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* Get to grips with key data analysis techniques* See specialized analysis techniques centered around machine learning* Master the effective visualization of your data* Work with the Java APIs and techniques used to perform data analysisIn DetailData science is concerned with extracting knowledge and insights from a wide variety of data sources to analyze patterns or predict future behavior. It draws from a wide array of disciplines including such fields as 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 so you understand 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 then 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.The book ends with a discussion on the resources that support the parallel implementation of many of these techniques and then a conclusion where more in-depth problem are illustrated. Due to the nature of the topic, simple examples of a technique are presented early followed by a more detailed treatment later in the book. This permits a more natural and smooth introduction to the techniques and flow in the book.