Episode 9 — 1.1 Recognize Data Types: Strings, Nulls, Numerics, Datetimes, Identifiers
This episode focuses on data types as the foundation of clean analysis and correct interpretation in Data+ DA0-002. You will separate common types and the mistakes that come from treating them casually: strings that look numeric, nulls that represent different kinds of missingness, numerics that require correct precision, datetimes that depend on format and timezone, and identifiers that must remain labels rather than quantities. You will learn why type awareness changes everything from aggregations to joins to visualizations, and why many “wrong” answers stem from a type assumption that was never tested. The goal is to quickly recognize type cues in a prompt and anticipate what could go wrong if types drift during ingestion or transformation.
You will work through scenarios that show how type problems surface in practice and in exam questions: leading zeros disappearing in IDs, dates swapping month and day, nulls turning into empty strings, and mixed-type columns producing unexpected sorting and filtering behavior. You will also cover a practical verification pattern: check a small sample, confirm counts of nulls, test a conversion, and re-check distributions to ensure the change matches intent. You will learn how to describe type decisions clearly, including when to keep a value as text for safety, and how to track type transformations so downstream consumers can trust the results. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
