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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

Product type Paperback

Published in Jan 2016

Publisher

ISBN-13 9781785281372

Length 416 pages

Edition 1st Edition

Languages

Scala

Concepts

Application Development

Author (1):

Pascal Bugnion

View More author details

Table of Contents (17) Chapters

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

References

This Wikipedia page gives information on semantic URLs: https://en.wikipedia.org/wiki/Semantic_URL and http://apiux.com/2013/04/03/url-design-restful-web-services/.
For a much more in depth discussion of the Play framework, I suggest Play Framework Essentials by Julien Richard-Foy.
REST in Practice: Hypermedia and Systems Architecture, by Jim Webber, Savas Parastatidis and Ian Robinson describes how to architect REST APIs.

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Authors (1)

Bugnion

Bugnion

Pascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University. Besides Scala, Pascal is a keen Python developer. He has contributed to NumPy, matplotlib and IPython. He also maintains scikit-monaco, an open source library for Monte Carlo integration. He currently lives in London, UK.

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