Many thanks to all who attended the talk @jayjacobs & I gave at RSA on Tuesday, February 26, 2013. It was really great to be able to talk to so many of you afterwards as well.
We’ve enumerated quite a bit of non-slide-but-in-presentation information that we wanted to aggregate into a blog post so you can viz along at home. If you need more of a guided path, I strongly encourage you to take a look at some of the free courses over at Coursera.
For starters, here’s a bit.ly bundle of data analysis & visualization bookmarks that @dseverski & I maintain. We’ve been doing (IMO) a pretty good job adding new resources as they come up and may have some duplicates to the ones below.
People Mentioned
- Stephen Few’s Perceptual Edge blog : Start from the beginning to learn from a giant in information visualization
- Andy Kirk’s Visualising Data blog (@visualisingdata) : Perhaps the quintessential leader in the modern visualization movement.
- Mike Bostock’s blog (@mbostock) : Creator of D3 and producer of amazing, interactive graphics for the @NYTimes
- Edward Tufte’s blog : The father of what we would now identify as our core visualization principles & practices
Tools Mentioned
- R : Jay & I probably use this a bit too much as a hammer (i.e. treat ever data project as a nail) but it’s just far too flexible and powerful to not use as a go-to resource
- RStudio : An amazing IDE for R. I, personally, usually despise IDEs (yes, I even dislike Xcode), but RStudio truly improves workflow by several orders of magnitude. There are both desktop and server versions of it; the latter gives you the ability to setup a multi-user environment and use the IDE from practically anywhere you are. RStudio also makes generating reproducible research a joy with built-in easy access to tools like kintr.
- iPython : This version of Python takes an already amazing language and kicks it up a few notches. It brings it up to the level of R+RStudio, especially with it’s knitr-like iPython Notebooks for–again–reproducible research.
- Mondrian : This tool needs far more visibility. It enables extremely quick visualization of even very large data sets. The interface takes a bit of getting used to, but it’s faster then typing R commands or fumbling in Excel.
- Tableau : This tool may be one of the most accessible, fast & flexible ways to explore data sets to get an idea of where you need to/can do further analysis.
- Processing : A tool that was designed from the ground up to help journalists create powerful, interactive data visualizations that you can slipstream directly onto the web via the Processing.js library.
- D3 : The foundation of modern, data-driven visualization on the web.
- Gephi : A very powerful tool when you need to explore networks & create beautiful, publication-worthy visualizations.
- MongoDB : NoSQL database that’s highly & easily scaleable without a steep learning curve.
- CRUSH Tools by Google : Kicks up your command-line data munging.
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