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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Creating donut charts


Donut charts are similar to pie charts, but they are missing the middle section (hence the name donut). Some analysts prefer donut charts to pie charts because they do not emphasize the size of each piece within the chart and are easier to compare to other donut charts. They also provide the added advantage of taking up less space, allowing for more formatting options in the display.

In this example, we will assume our data is already populated in a two-dimensional array called ageCount. The first row of the array contains the possible age values, ranging again from 19 to 30 (inclusive). The second row contains the number of data values equal to each age. For example, in our dataset, there are six data values equal to 19, so ageCount[0][1] contains the number six.

We create a DataTable and use the add method to add our values from the array. Notice we are testing to see if the value of a particular age is zero. In our test case, there will be zero data values equal to...

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