Data science introductory courses might give you the impression that dealing with data is neat, tidy, and simple. They present you with a simplistic dataset and the scikit-learn or Pandas documentation, and a day or so later, you're done! Piece of cake, right? The real world of data isn't that easy! As a data scientist who has worked in the industry for several years, I have had a lot of experience dealing with messy, inaccurate, incomplete data, and I want to share those experiences with you. I'll talk my way through three real-world situations where I've had to analyze and build models on untidy and complex data, going through how I've preprocessed the data and prepared it for modeling. You'll leave with an understanding of how a data scientist thinks about data and what she does when the data is complicated.

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