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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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Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem 2. Data Acquisition FREE CHAPTER 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

Plotting your course

It's easy to overlook planning and preparation when you're preoccupied with experimenting on the latest technologies and data! Nevertheless, the process of how you write scalable algorithms is just as important as the algorithms themselves. Therefore, it's crucial to understand the role of planning in your project and to choose an operating framework that allows you to respond to the demands of your goals. The first recommendation is to adopt an agile development methodology.

The distinctive ebb and flow of analytic authoring may mean that there is just no natural end to the project. By being disciplined and systematic with your approach, you can avoid many pitfalls that lead to an under performing project and poorly performing code. Conversely, no amount of innovative, open source software or copious corpus will rescue a project with no structure.

As every data science project is slightly different, there's no right or wrong answers when it comes...

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