Episode 6 — 1.1 Decode Common File Extensions: CSV, XLSX, JSON, TXT, JPG, DAT
This episode helps you recognize what a file extension implies about data structure, parsing effort, and analysis risk, which is a frequent decision point in Data+ DA0-002 scenarios. You will translate common extensions into expectations you can act on: CSV as delimited rows that look simple but hide quoting and encoding traps, XLSX as spreadsheet data that often carries formatting baggage and multiple sheets, JSON as flexible nested objects that require path-based extraction, and TXT as “it depends,” where structure may exist but is not guaranteed. You will also cover why JPG is usually not a dataset in the traditional sense, even if it contains useful information, and why DAT is a warning sign that you must verify content before assuming structure. The emphasis stays on practical recognition: what you can reliably infer and what you cannot.
You will apply an intake routine that prevents common exam-and-workplace errors, such as assuming headers exist, treating strings as numbers, losing leading zeros, or breaking dates during conversion. You will walk through quick checks for delimiter consistency, encoding mismatches, hidden metadata lines, and “mixed type” columns that silently change behavior in tools. You will also practice deciding the safest transformation path when asked to standardize data for downstream querying or reporting, including when to preserve raw copies and when to normalize types early. 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.
