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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Summary


Data science is not just about machine learning. In fact, machine learning is only a small portion of it. In our understanding of what modern data science is, the science often happens exactly here, at the data enrichment process. The real magic occurs when one can transform a meaningless dataset into a valuable set of information and get new insights out of it. In this section, we have been describing how to build a fully functional data insight system using nothing more than a simple collection of URLs (and a bit of elbow grease).

In this chapter, we demonstrated how to create an efficient web scraper with Spark using the Goose library and how to extract and de-duplicate features out of raw text using NLP techniques and the GeoNames database. We also covered some interesting design patterns such as mapPartitions and Bloom filters that will be discussed further in Chapter 14, Scalable Algorithms.

In the next chapter, we will be focusing on the people we were able to extract from all...

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