- 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
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 Java-based 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 time-series data. This book will also show you how you can utilize different Java-based 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.
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 non-relational databases and from time-series data
- Employ Java tools to visualize data in various forms
- Understand multimedia data analysis algorithms and implement them in Java.
About the Author
John R. Hubbard has been doing computer-based data analysis for over 40 years at colleges and universities in Pennsylvania and Virginia. He holds an MSc in computer science from Penn State University and a PhD in mathematics from the University of Michigan. He is currently a professor of mathematics and computer science, Emeritus, at the University of Richmond, where he has been teaching data structures, database systems, numerical analysis, and big data.
Dr. Hubbard has published many books and research papers, including six other books on computing. Some of these books have been translated into German, French, Chinese, and five other languages. He is also an amateur timpanist.
Table of Contents
Chapter 1. Introduction to Data Analysis
Chapter 2. Data Preprocessing
Chapter 3. Data Visualization
Chapter 4. Statistics: Elementary statistical methods and their implementation in Java
Chapter 5. Relational Database Access
Chapter 6. Regression Analysis
Chapter 7. Classification Analysis
Chapter 8. Cluster Analysis
Chapter 9. Recommender Systems
Chapter 10. Working with NoSQL Databases
Chapter 11. Big Data Analysis with Java