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I’m excited to finally share some news: I’ve resigned my position on the NYU faculty and started working full time as Vice President of Information Design at Nomic, a startup helping people explore, visualize, and interact with massive vector datasets in their browser.
When you teach programming skills to people with the goal that they’ll be able to use them, the most important obligation is not to waste their time or make things seem more complicated than they are. This should be obvious. But when I’m helping humanists decide what workshops to take, reviewing introductory materials for classes, or browsing tutorials to adapt for teaching, I see the same violation of the principle again and again. Introductory tutorials waste enormous amounts of time vainly covering ways of accomplishing tasks that not only have absolutely no use for beginners, but which will confuse learners by making them
I’ve recently been getting pretty far into the weeds about what the future of data programming is going to look like. I use pandas and dplyr in python and R respectively. But I’m starting to see the shape of something that’s interesting coming down the pike. I’ve been working on a project that involves scatterplot visualizations at a massive scale–up to 1 billion points sent to the browser. In doing this, two things have become clear: