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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
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
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Jennifer L. Reese
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Toc

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

Standard Java library


The standard Java library is very rich and offers a lot of tools for data manipulation, including:

  • Collections for organizing data in memory
  • I/O for reading and writing data
  • Streaming APIs for making data transformations easy

In this chapter, we will look at all these tools in detail. 

Collections

Data is the most important part of data science. When dealing with data, it needs to be efficiently stored and processed, and for this we use data structures. A data structure describes a way to store data efficiently to solve a specific problem, and the Java Collection API is the standard Java API for data structures. This API offers a wide variety of implementations that are useful in practical data science applications.

We will not describe the collection API in full detail, but concentrate on the most useful and important ones--list, set, and map interfaces.

Lists are collections where each element can be accessed by its index. The g0-to implementation of the List interface...

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