Good models Bad data Bad analysis

One of the key themes in Numbersense is the relationship between models and data. Think of data as inputs to models which generate outputs (predictions, etc.). A lot of the dialog in the data science community revolves around models, or algorithms that implement underlying models (random forests, deep learning, etc.). But there are countless examples of applying good models to bad data, resulting in bad outputs. I just finished teaching a class about Analytical Models at Columbia.