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.
Brian Korzynski is a Senior Machine Learning Engineer at one of Detroit's best companies, United Shore. He has had an extensive career in Microsoft technologies working for a variety of companies both large and small. Working in many industries such as logistics, manufacturing, compliance, and finance has given him a wide variety of knowledge and skills that he wants to share with the world. Currently he is working on a variety of machine learning and artificial intelligence applications to aid in the mortgage process.