Twitter has become a lean, mean data-collecting machine, and with this entertaining video course, you'll learn techniques for mining this vast wealth of information. Follow along as author and data analyst Matthew Russell shows O'Reilly's Director of Market Research how easy it is to uncover valuable Twitter data with basic Python tools and pragmatic storage technologies such as Redis and CouchDB.
Matthew analyzes the Twitter stream of top-tweeter Tim O'Reilly, looks in-depth into a friendship network, and considers Freakonomic questions such as "What does Justin Bieber have in common with the Tea Party?" Based on portions of Matthew’s book, Mining the Social Web (O'Reilly, 2011), this fast-moving presentation is ideal for beginning to intermediate programmers, as well as data analysts, who want to find extraordinary nuggets of information in the Twitter data haystack.
Overview 13m 42s
Tweets, Trends and Retweet Visualizations 12m 40s
Tweets, Trends and Retweet Visualizations Part 2 25m 06s
Friends, Followers and Setwise Operations 21m 07s
Friends, Followers and Setwise Operations Part 2 32m 43s
Friends, Followers and Setwise Operations Part 3 22m 29s
The Tweet, The Whole Tweet and Nothing but The Tweet 22m 39s
The Tweet, The Whole Tweet and Nothing but The Tweet Part 2 29m 16s
Matthew Russell, Vice President of Engineering at Digital Reasoning Systems (http://www.digitalreasoning.com/) and Principal at Zaffra (http://zaffra.com), is a computer scientist who is passionate about data mining, open source, and web application technologies. He’s also the author of Dojo: The Definitive Guide (O’Reilly).