Identify what each row, column, and cell represents in an everyday table. Practice naming the observation unit so you know exactly what one record is about.

Compare a customer list, a purchase log, and a daily weather table to see how entities, events, and snapshots create different kinds of datasets. Reason through why changing the row grain changes the questions the data can answer.

Decode columns as variables with values that may be numbers, categories, dates, IDs, or text labels. Use simple examples to see why labels and codes need definitions before they can be analyzed safely.

Apply the previous explanations in a guided problem.

Attach units, time windows, and measurement scales to numbers before comparing them. Recognize when two columns that look similar are actually measuring different things.

Distinguish missing, zero, unknown, not applicable, and not collected. Reason through how different kinds of missing values can change what a dataset seems to say.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Trace how a real-world idea becomes a recorded measurement through definitions, instruments, forms, sensors, or human choices. Spot measurement error, rough proxies, and selection bias in everyday data sources.

Use metadata such as a data dictionary, source notes, dates, population, and collection method to judge the dataset’s boundaries. Decide which claims the data can support and which would require more evidence.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Review this chapter with practice based on your mistakes.