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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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Toc

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

Modular JavaScript through RequireJS

The simplest way of injecting JavaScript libraries into the namespace is to add them to the HTML framework via <script>...</script> tags in the HTML header. For instance, to add JQuery, we would add the following line to the head of the document:

<script src=@routes.Assets.versioned("lib/jquery/jquery.js") type="text/javascript"></script>

While this works, it does not scale well to large applications, since every library gets imported into the global namespace. Modern client-side JavaScript frameworks such as AngularJS provide an alternative way of defining and loading modules that preserve encapsulation.

We will use RequireJS. In a nutshell, RequireJS lets us encapsulate JavaScript modules through functions. For instance, if we wanted to write a module example that contains a function for hiding a div, we would define the module as follows:

// example.js
define(["jquery", "underscore"], function...
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