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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
Tools
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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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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

Chapter 24. Scaling Data Science

So far we have covered a lot of material about data science, we learned how to do both supervised and unsupervised learning in Java, how to perform text mining, use XGBoost and train Deep Neural Networks. However, most of the methods and techniques we used so far were designed to run on a single machine with the assumption that all the data will fit into memory. As you should already know, this is often the case: there are very large datasets that are not possible to process with traditional techniques on a typical hardware. 

In this chapter, we will see how to process such datasets--we will look at the tools that allow processing the data across several machines. We will cover two use cases: one is large scale HTML processing from Common Crawl - the copy of the Web, and another is Link Prediction for a social network.

We will cover the following topics:

  • Apache Hadoop MapReduce
  • Common Crawl processing
  • Apache Spark 
  • Link prediction
  • Spark GraphFrame and MLlib libraries...
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