Read any dataset's shape by one test, does scrambling its order destroy it, then match tabular, image, text, and time-series to the job each shape can feed.

A spreadsheet, a cat photo, a stock chart: three datasets, three shapes. Scramble each one's insides and only the spreadsheet, rows like `price=340k`, survives while the other two turn to noise, telling you what you hold.

Hold back a test set so a score means something, split a table at random but a stock chart by time, and catch the leak that makes a broken model look perfect.

A model scores 99% on the house table it studied, rows like `price=340k`, then flops on the next real listing. It recited rows it had seen, and one slice held back from the start keeps the score honest.