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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment 2. Driving Visual Analysis with Automobile Data with R FREE CHAPTER 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Introduction


Social networks have become a fixture of modern life, thanks to social networking sites such as Facebook and Twitter. Social networks themselves are not new, however. The study of such networks dates back to the early twentieth century, particularly in the fields of sociology and anthropology. It is their prevalence in mainstream applications that has moved these types of studies to the purview of data science.

It turns out that social networks are extremely interesting as models for human behavior. Human civilization stems from tribal societies and as a result, Dunbar's number-a hypothesis that at any given time we can only have 150 people in our extended social network-has famously been proven through the analysis of the most active networks. Latent social networks exist everywhere, not just in popular web 2.0 applications. We manage our lives through connections to various networks, and, because of that, we generate a lot of related, rich data that can be used to make predictions...

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