Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: 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. Para 2: 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. Para 3: 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. para 4: 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
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Chapter 12. Bringing It All Together

While we have demonstrated many aspects of using Java to support data science tasks, the need to combine and use these techniques in an integrated manner exists. It is one thing to use the techniques in isolation and another to use them in a cohesive fashion. In this chapter, we will provide you with additional experience with these technologies and insights into how they can be used together.

Specifically, we will create a console-based application that analyzes tweets related to a user-defined topic. Using a console-based application allows us to focus on data-science-specific technologies and avoids having to choose a specific GUI technology that may not be relevant to us. It provides a common base from which a GUI implementation can be created if needed.

The application performs and illustrates the following high-level tasks:

  • Data acquisition

  • Data cleaning, including:

    • Removing stop words

    • Cleaning the text

      • Sentiment analysis

      • Basic data statistic collection...