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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

Your Secure Responsibility


Now that we've covered the common security use cases and discussed some of the tools that a data scientist needs to be aware of in their everyday activities, there's one last important item to note. While in their custody, the responsibility for data, including its security and integrity, lies with the data scientist. This is usually true whether or not you are explicitly told. Therefore, it is crucial that you take this responsibility seriously and take all the necessary precautions when handling and processing data. If needed, also be ready to communicate to others their responsibility. We all need to ensure that we are not held responsible for a breach off-site; this can be achieved by highlighting the issue or, indeed, even having a written contract with the off-site service provider outlining their security arrangements. To see a real-world example of what can go wrong when you don't pay proper attention to due diligence, have a look at some security notes...

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