Data journalism at the Guardian: what is it and how do we do it?
Here's an interesting thing: data journalism is becoming part of the establishment. Not in an Oxbridge elite kind of way (although here's some data on that) but in the way it is becoming the industry standard. Two years ago, when we launched the Datablog, all this was new. People still asked if getting stories from data was really journalism and not everyone had seen Adrian Holovaty's riposte. But
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pachube :: connecting environments, patching the planet - Map of feeds
Welcome to Pachube, a service that enables people to tag and share real time environmental data from objects, devices and spaces around the world. The key aim is to facilitate interaction between remote environments, both physical and virtual. Click markers above for feed information Total feeds: 119 Total users: 182 Comments or feedback? Tell us! If you have a: device, building or interactive in
Data Wrangler
Wrangler is an interactive tool for data cleaning and transformation. Spend less time formatting and more time analyzing your data. UPDATE: The Stanford/Berkeley Wrangler research project is complete, and the software is no longer actively supported. Instead, we have started a commercial venture, Trifacta. For the most recent version of the tool, see the free Trifacta Wrangler. Why wrangle? Too mu
Data Analysts Captivated by R’s Power (Published 2009)
R first appeared in 1996, when the statistics professors Robert Gentleman, left, and Ross Ihaka released the code as a free software package.Credit...Left, Stuart Isett for The New York Times; right, Kieran Scott for The New York Times To some people R is just the 18th letter of the alphabet. To others, it’s the rating on racy movies, a measure of an attic’s insulation or what pirates in movies sa
Lecture 1 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive
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