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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

Chapter 13. Web APIs with Play

In the first 12 chapters of this book, we introduced basic tools and libraries for anyone wanting to build data science applications: we learned how to interact with SQL and MongoDB databases, how to build fast batch processing applications using Spark, how to apply state-of-the-art machine learning algorithms using MLlib, and how to build modular concurrent applications in Akka.

In the last chapters of this book, we will branch out to look at a web framework: Play. You might wonder why a web framework would feature in a data science book; surely such topics are best left to software engineers or web developers. Data scientists, however, rarely exist in a vacuum. They often need to communicate results or insights to stakeholders. As compelling as an ROC curve may be to someone well versed in statistics, it may not carry as much weight with less technical people. Indeed, it can be much easier to sell insights when they are accompanied by an engaging...

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