The promise of accelerated computing presents an interesting paradox: while no one complains when new compute infrastructure is dramatically faster than its predecessor, few people realize how much they’d benefit from acceleration until they have it. It is perhaps unsurprising that a data scientist’s daily work consists of tasks that they can accomplish with their available computing resources, but simply running our existing work faster makes acceleration into a mere luxury. For accelerated computing to fulfill its promise, we need it to transform our work by enabling us to do new things that wouldn’t have been feasible without it. In this talk, we’ll discuss our experiences accelerating data science with specialized hardware and by scaling out on clusters. We’ll present examples of previously-impossible techniques becoming feasible, of the pleasant luxury of improved performance, and of the data science tasks that aren’t likely to justify additional hardware or implementation effort. You’ll leave this talk with a better understanding of how accelerated and scale-out computing can fit into your data science practice, a catalog of techniques that are still well served by standard hardware, and some actionable advice for how to take advantage of parallel and distributed computing across your workflow.