What exactly can you do with Python?

Depending on whom you ask you may see these two separated… or not. From a purpose standpoint it makes sense to separate ML and DA, but when talking strictly about technology, they share a lot of the stack. Given the fact that the base is the same, I'll put ML and DA in one shared basket, but be warned: it's a very, very large basket. Let's start with the core of working with data: reading, writing and manipulation. There are two libraries you need to know to even get started: Numpy and Pandas.