Data science seems to be all the rage these days, but there are a lot of misconceptions around what this means, since everyone seems to have their own definition and there is no text book definition per say. We will touch briefly on the techniques and tools (including machine learning and big data), but focus more on the process itself and define what all is composed within data science and its related projects. Examples of these are preparing data, separating machine learning problems from programming problems, defining the process to follow, and dealing with a massive amount of variance in your data.