In his essay "What I Do Before a Data Science Project to Ensure Success," Eugene Yan makes the case that the fastest path to a completed project may not involve starting immediately. He warns that haste makes waste, and that diving directly into a data science problem often creates more work later.
The central idea is that preparation matters. Rather than treating the start of a project as a race to model training, Yan advocates for an intentional phase of groundwork so that the actual execution is smoother and more likely to succeed.
The piece is a reminder that in applied machine learning, front-loaded effort can be an investment. It positions slower, more careful upfront thinking as a practical strategy rather than a luxury.