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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Toc

Table of Contents (13) Chapters Close

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

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...
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