Java for Data Science

Key Features

• 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

Book Description

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.

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

Table of Contents

Chapter 1. Getting Started with Data Science
Chapter 2. Data Acquisition
Chapter 3. Data Cleaning
Chapter 4. Data Visualization
Chapter 5. Statistical Data Analysis Techniques
Chapter 6. Machine Learning
Chapter 7. Neural Networks
Chapter 8. Deep Learning
Chapter 9. Text Analysis
Chapter 10. Visual and Audio Analysis
Chapter 11. Mathematical and Parallel Techniques for Data Analysis
Chapter 12. Bringing It All Together