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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

Summary


In this chapter, we introduce basic graphs, plots, and charts used to visualize data. The process of visualization enables an analyst to graphically examine the data under review. This is more intuitive, and often facilitates the rapid identification of anomalies in the data that can be hard to extract from the raw data.

Several visual representations were examined, including line charts, a variety of bar charts, pie charts, scatterplots, histograms, donut charts, and bubble charts. Each of these graphical depictions of data provides a different perspective of the data being analyzed. The most appropriate technique depends on the nature of the data being used. While we have not covered all of the possible graphical techniques, this sample provides a good overview of what is available.

We were also concerned with how Java is used to draw these graphics. Many of the examples used JavaFX. This is a readily available tool that is bundled with Java SE. However, there are several other libraries...

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