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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 FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 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


In this chapter, we are going to dive into the world of social media analysis through the use of RESTful web service APIs. Twitter is a microblogging social network whose stream is invaluable for data mining, particularly text mining, and they have an excellent API that we will learn how to interact with via Python. We will use the API to fetch Twitter social connections and collect and store JSON data using both traditional file storage and the popular NoSQL database, MongoDB. Our analysis will attempt to ascertain the geographic location of connections and produce visualization from the data.

Throughout the chapter, you should begin to notice patterns about how APIs are designed and their intended use. Interaction with APIs is an extremely important data science topic, and having a solid understanding of them will unlock a whole new world of data upon which you can perform a myriad of analyses.

API stands for Application Programming Interface, and in traditional computer science...

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