Gabriel Gonzales: Data science APIs in Haskell - λC Winter Retreat 2017

Gabriel Gonzales surveys the Haskell data science ecosystem and suggests how to design user-friendly APIs using mathematical principles.

Data science is one programming area where Haskell's data-driven approach should be a natural fit but hasn't quite taken off just yet. However, the language has recently made several leaps and bounds in this area and most of the lower-level machinery is there, although perhaps not as slick as their counterparts in more mature languages. This session surveys the existing ecosystem of libraries and tools in order to offer some suggestions for how we might be able to improve upon the user experience. This builds upon my first "Applied category theory and abstract algebra" session by taking inspiration from mathematics to design user-friendly interfaces. However, the other session is not necessarily a prerequisite and familiarity with Haskell's Typeclassopedia would also suffice to follow along. You should attend this session if you are interested in using Haskell for data science or you are interested in authoring a library or tool of your own. By the end of this session, you should be better equipped to use Haskell for small-scale data science (but not necessarily big data).