Cutting a lesson down — Rows, grain, and the shape of a dataset

A note from writing Rows, grain, and the shape of a dataset, which took three passes to get to something short.

Nearly every wrong number in a career traces back to someone not knowing what one row of their table represents.

The hard part of writing this was cutting it. The first version covered every case; the useful version covers the case you hit on a Tuesday and names the rest in a sentence. Completeness is a property of reference material, not of teaching material, and confusing the two produces something nobody finishes.

It sits in the Foundations module, and the exercise runs against retail-orders. That pairing is deliberate: the dataset was built with the trap the lesson describes already in it, so the exercise fails in the instructive way rather than the confusing one.

Ordering matters here more than in most courses. It follows What a data team is actually for and leads into Turning a request into a question, and reading it out of sequence mostly works but costs you the setup.

Free means free, and it also means we can rewrite it whenever it is wrong. No edition, no errata PDF, nothing to repurchase. The whole course is 36 lessons and the fixes land the day we find them.