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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
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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 3. Plotting with breeze-viz

Data visualization is an integral part of data science. Visualization needs fall into two broad categories: during the development and validation of new models and, at the end of the pipeline, to distill meaning from the data and the models to provide insight to external stakeholders.

The two types of visualizations are quite different. At the data exploration and model development stage, the most important feature of a visualization library is its ease of use. It should take as few steps as possible to go from having data as arrays of numbers (or CSVs or in a database) to having data displayed on a screen. The lifetime of graphs is also quite short: once the data scientist has learned all he can from the graph or visualization, it is normally discarded. By contrast, when developing visualization widgets for external stakeholders, one is willing to tolerate increased development time for greater flexibility. The visualizations can have significant...

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