In Defense of Model Simplicity: Examples from Laura McLay about problems in data science and optimization that respond better to a simple model than a complex one.

Learning from the Best: Good write-up on Kaggle’s blog about suggestions from past data science competition winners. Spend the most time focusing on extracting the right features to solve the problem. Without the right features, it doesn’t matter how complex your model is, it won’t work.

Albert Einstein: “Everything should be made as simple as possible, but not simpler.”