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3D animated filmmaking in free software and the commons

3D animated filmmaking in free software and the commons

The Tube Open Movie by Bassam Kurdali » Updates Friends! Supporters! Please pardon the radio silence while we've been cranking frenetically to get the movie made. Conducting such an ambitious project with a tiny budget means that we all work on Tube with one hand while also keeping the lights on with the other. Our lovely crew is pushing hard to ready the trailer for release in time for the Siggraph conference next week, which five of Tube's artists (Bassam, Pablo, Hanny, Francesco, and Bing-Run) will take a few days out to attend. To whet the appetite, here are a few render tests from the work that's been in-progress, as well as a fast look at some of what's been happening: Between inescapable bouts of his trademark rigging, Bassam's screens are full with a mix of directing, project management, shading tasks, time-lapse animation, pipeline coding, and more. A great group of super-talented artists and interns have joined our local crew both visiting from abroad and online.

A Taxonomy of Data Science Posted: September 25th, 2010 | Author: Hilary Mason | Filed under: Philosophy of Data | Tags: data, data science, osemn, taxonomy | 31 Comments Both within the academy and within tech startups, we’ve been hearing some similar questions lately: Where can I find a good data scientist? What do I need to learn to become a data scientist? Or more succinctly: What is data science? We’ve variously heard it said that data science requires some command-line fu for data procurement and preprocessing, or that one needs to know some machine learning or stats, or that one should know how to `look at data’. Different data scientists have different levels of expertise with each of these 5 areas, but ideally a data scientist should be at home with them all. We describe each one of these steps briefly below: Obtain: pointing and clicking does not scale. Deep thoughts: Our next post addresses how one goes about learning these skills, that is: “what does a data science curriculum look like?”

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