Book description
Get the most out of the popular Java libraries and tools to perform efficient data analysis
About This Book
 Get your basics right for data analysis with Java and make sense of your data through effective visualizations.
 Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning.
 This is your companion to understanding and implementing a solid data analysis solution using Java
Who This Book Is For
If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required.
What You Will Learn
 Develop Java programs that analyze data sets of nearly any size, including text
 Implement important machine learning algorithms such as regression, classification, and clustering
 Interface with and apply standard open source Java libraries and APIs to analyze and visualize data
 Process data from both relational and nonrelational databases and from timeseries data
 Employ Java tools to visualize data in various forms
 Understand multimedia data analysis algorithms and implement them in Java.
In Detail
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks.
This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression.
In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Javabased tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and timeseries data. This book will also show you how you can utilize different Javabased libraries to create insightful and easy to understand plots and graphs.
By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Style and approach
The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient realworld examples and use cases are included to help you grasp the concepts quickly and apply them easily in your daytoday work. Packed with clear, easytofollow examples, this book will turn you into an ace data analyst in no time.
Publisher resources
Table of contents

Java Data Analysis
 Table of Contents
 Java Data Analysis
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Customer Feedback
 Preface
 1. Introduction to Data Analysis
 2. Data Preprocessing
 3. Data Visualization

4. Statistics
 Descriptive statistics
 Random sampling
 Random variables
 Probability distributions
 Cumulative distributions
 The binomial distribution
 Multivariate distributions
 Conditional probability
 The independence of probabilistic events
 Contingency tables
 Bayes' theorem
 Covariance and correlation
 The standard normal distribution
 The central limit theorem
 Confidence intervals
 Hypothesis testing
 Summary
 5. Relational Databases
 6. Regression Analysis
 7. Classification Analysis
 8. Cluster Analysis
 9. Recommender Systems
 10. NoSQL Databases
 11. Big Data Analysis with Java
 A. Java Tools
 Index
Product information
 Title: Java Data Analysis
 Author(s):
 Release date: September 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787285651
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