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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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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

FEC data


In this chapter, we will use a somewhat more involved example dataset. The Federal Electoral Commission of the United States (FEC) records all donations to presidential candidates greater than $200. These records are publicly available. We will look at the donations for the campaign leading up to the 2012 general elections that resulted in Barack Obama's re-election. The data includes donations to the two presidential candidates, Obama and Romney, and also to the other contenders in the Republican primaries (there were no Democrat primaries).

In this chapter, we will take the transaction data provided by the FEC, store it in a table, and learn how to query and analyze it.

The first step is to acquire the data. If you have downloaded the code samples from the Packt website, you should already have two CSVs in the data directory of the code samples for this chapter. If not, you can download the files using the following links:

  • data.scala4datascience.com/fec/ohio.csv.gz (or ohio.csv...

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