Java for Data Science: Explore, Analyse and Visualise Data Effectively Using Java Tools and Techniques

Harness the incredible power of Java-based approaches to data science and create new, innovative applications to explore, visualise and analyse big data. With its tutorial approach and step-by-step instructional style, Java for Data Science is the ultimate data science book for Java developers interested in Java-based data science solutions.

Summary

Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning.

About the Technology

The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning.

About the Book

Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning.

What’s Inside

• Understand data science principles with Java support
• Discover machine learning and deep learning essentials
• Explore data science problems with Java-based solutions
  

About the Reader

With its tutorial approach, this data science book has been written for experienced Java programmers who want to better understand the field of data science and learn how Java supports its underlying techniques. The step-by-step instructional style also makes Java for Data Science ideal for beginners, allowing you to get up and running quickly.

About the Author

Richard M. Reese has worked in software development supervision & training for 17 years, and currently teaches at Tarleton State University. He has written several Java books on topics including certification, natural language processing, functional programming, and networks.

Jennifer L. Reese currently teaches Computer Science to high school students, having studied Computer Science and earned her M.Ed. from Tarleton in December 2016.

Table of Contents

• Getting Started with Data Science
• Data Acquisition
• Data Cleaning
• Data Visualization
• Statistical Data Analysis Techniques
• Machine Learning
• Neural Networks
• Deep Learning
• Text Analysis
• Visual and Audio Analysis
• Mathematical and Parallel Techniques for Data Analysis
• Bringing It All Together