Modern data is increasing very large in terms of both the number of objects and the number of dimensions. While in a statistical sense massive amounts of data make nonparametric methods entirely appropriate, their computational cost has made practitioners typically conclude that they are not possible in such scenarios. I will review a few common conceptual classes of nonparametric methods, including both classical and modern variants, then review recent algorithmic advances which can make nonparametric methods tractable