Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Spark for Data Science

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

Arrow left icon
Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
Arrow right icon
Authors (5):
Arrow left icon
David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
Author Profile Icon Matthew Hallett
Matthew Hallett
Antoine Amend Antoine Amend
Author Profile Icon Antoine Amend
Antoine Amend
Andrew Morgan Andrew Morgan
Author Profile Icon Andrew Morgan
Andrew Morgan
Albert Bifet Albert Bifet
Author Profile Icon Albert Bifet
Albert Bifet
+1 more Show less
Arrow right icon
View More author details
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

Chapter 4. Exploratory Data Analysis

Exploratory Data Analysis (EDA) performed in commercial settings is generally commissioned as part of a larger piece of work that is organized and executed along the lines of a feasibility assessment. The aim of this feasibility assessment, and thus the focus of what we can term an extended EDA, is to answer a broad set of questions about whether the data examined is fit for purpose and thus worthy of further investment.

Under this general remit, the data investigations are expected to cover several aspects of feasibility that include the practical aspects of using the data in production, such as its timeliness, quality, complexity, and coverage, as well as being appropriate for the intended hypothesis to be tested. While some of these aspects are potentially less fun from a data science perspective, these data quality led investigations are no less important than purely statistical insights. This is especially true when the datasets in question...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime