Functional programming is all the rage at the moment, with solid promises and guarantees and a mathematical approach to programming, functional has a huge appeal to data-scientists of all backgrounds. One might even ask, whether focussing on Python is the right choice, given the momentum of functional programming languages have for data science platforms.
In this talk I will present on the less-obvious aspects of functional programming, how they have shaped the PyData stack right from the beginning, I will show how functional data-science libraries (dask) and frameworks (PySpark) will let Python stay at least as relevant tomorrow, as it is for productive data-science and data-analysis today.
We'll learn how abstractions of functional programming can be used to built powerful abstractions over the machine, and liberate us from the constraints of the von Neumann computer.
