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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Summary

In this chapter, we learned how to write a fully-featured web application with the Play framework. Congratulations on making it this far. Building web applications are likely to push many data scientists beyond their comfort zone, but knowing enough about the web to build basic applications will allow you to share your results in a compelling, engaging manner, as well as facilitate communications with software engineers and web developers.

This concludes our whistle stop tour of Scala libraries. Over the course of this book, we have learned how to tackle linear algebra and optimization problems efficiently using Breeze, how to insert and query data in SQL databases in a functional manner, and both how to interact with web APIs and how to create them. We have reviewed some of tools available to the data scientist for writing concurrent or parallel applications, from parallel collections and futures to Spark via Akka. We have seen how pervasive these constructs are in Scala libraries...

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