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Estimating how long something is going to take is a part of everyday life, and as most of us know, we humans stink at it. Yet as software developers, estimating how long features and functionality will take to develop is all part of the job description. I was explaining Red Badger’s project sizing technique the other day to a client who noted that we had a “very scientific” process. I think what they really meant was that it appeared very mathematical and exact (which it isn’t and beware anyone who tries to convince you that estimating can be), but taking the comment at face value, they had inadvertently hit upon something.
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.
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.
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.
“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.