Datalinked. De. Extraction. Red Badger | All posts by davidwynne. Red Badger recently switched away from Exchange Server to Google Apps and, after 5ish months, I can honestly say I think it’s one of the best decisions we’ve taken and we are collectively more productive because of it. It was with some interest and surprise then that I was reading the hacker news thread last week about how Google have “ruined” Gmail with the latest tweaks.
I guess I’ll preface this and say that until Red Badger moved to Google Apps I wasn’t a regular user of Gmail and so have pretty much learnt my way around based on the app as it stands now. I know a few long-time Gmail users in the Red Badger office have moaned about the new compose window etc, but I guess what you never had, you never missed and it’s never bothered me. I like it. When I first read that, I guess I thought of Gmail. Learning keyboard shortcuts can be a bit daunting. Make sure you’ve enabled all the keyboard shortcuts first. Empty Inbox This is where all of this is heading. Mailbox Triage Short Term Labels. OCDQ - Obsessive-Compulsive Data Quality (OCDQ) by Jim Harris.
Václav Pech. Continue with the data flow I can't resist sharing some more exciting details about the options GPars dataflow concurrency brings to the table. Remember the intro I posted recently about the concepts and basic usage scenarios? And the GPars to GoLang comparison post moaning about blocking threads on dataflow variable reads? Well, that was just the first part of a longer story to whet your appetite. Check out some more ways to make your Groovy or Java data flow. Brief recap Last time we've seen a typical usage pattern - multiple concurrently-run logical tasks exchanging data through dataflow variables: task { mass << calculateMass(volume.val, density.val)} task { volume << calculateVolume()} task { density << calculateDensity()} Since dataflow variables are essentially single-assignment, multiple-read thread-safe variables, the tasks, as thus the underlying threads, get scheduled automatically to fullfill the requested concurrent scenario without any user intervention.
Wasting threads Example. Your Random Numbers – Getting Started with Processing and Data Visualization. Over the last year or so, I’ve spent almost as much time thinking about how to teach data visualization as I’ve spent working with data. I’ve been a teacher for 10 years – for better or for worse this means that as I learn new techniques and concepts, I’m usually thinking about pedagogy at the same time. Lately, I’ve also become convinced that this massive ‘open data’ movement that we are currently in the midst of is sorely lacking in educational components. The amount of available data, I think, is quickly outpacing our ability to use it in useful and novel ways. How can basic data visualization techniques be taught in an easy, engaging manner? This post, then, is a first sketch of what a lesson plan for teaching Processing and data visualization might look like. Let’s Start With the Data We’re not going to work with an old, dusty data set here.
Even on a Saturday, a lot of helpful folks pitched in, and I ended up with about 225 numbers. It’s about time to get down to some coding. OK. Data visualization for the social networked impaired. I've been thinking a lot about data visualization, meaning things like the Internet version of those USA Today infographics and the data you get back from your site traffic analytics group. But data viz is not just about fancy Excel charts and animation; it's a discipline used to visualize information of any sort and it's becoming an increasingly important way to communicate with your audience. Its importance, I believe, lies in its ability to reach scale, that is, helping marketers get the biggest audience possible.
Our business is all about scale and we have metrics to measure it. But scale is about more than metrics -- it's about having a cultural impact, having consumers feel they're part of something larger. Using data visualization, for instance, Domino's makes it fun to track the pizza-manufacturing process.
GE visualizes health data very well. Sometimes it's a big part of the experience; Digg's community, for instance, is entirely based on "how many, how much. " But what to do? Visualiser l’économie comme les “quants” | Owni.fr. “Quants” est le surnom donné aux analystes quantitatifs, qui manipulent au quotidien un nombre important de données dans le domaine des mathématiques financières. L’une des explications de la crise que traverse l’économie mondiale vient certainement se nicher dans les modèles utilisés par ces professionnels de la finance, dont les compétences vont des mathématiques à la physique [...]
“Quants” est le surnom donné aux analystes quantitatifs, qui manipulent au quotidien un nombre important de données dans le domaine des mathématiques financières. L’une des explications de la crise que traverse l’économie mondiale vient certainement se nicher dans les modèles utilisés par ces professionnels de la finance, dont les compétences vont des mathématiques à la physique en passant par les probabilités. Un univers étrange, que les néerlandais de onesize se sont attachés à faire vivre dans le cadre d’un documentaire sur les “quants”. Sur ce, bon week-end à toutes et tous :-) Laurent Lourenço» exemple visualisation données. Dropped Domains | Register Domains » Horizon Graph data visualization from Panopticon Software.