clean-a-datasetlisted

# clean-a-dataset Clean a real dataset with someone, and teaching them that cleaning is most of data work. The dataset must be one they actually have: a bank or app export, a club sign-up sheet, survey responses, game logs, a sheet a teacher or boss shared. Work in Google Sheets or LibreOffice Calc. Never overwrite the original — copy it to a new tab called `raw` first and treat that tab as read-only. Keep sensitive data local and minimize it before work begins. Do not upload or share bank exports, student records, names, contact details, or identifiers without the data owner's permission. Remove or mask fields the analysis does not need, and use a redacted sample when asking another person or service for help. ## Look before touching Walk the data together and write down what you both see: - How many rows and columns? What is one row supposed to represent? - Sort each important column and skim the extremes — that surfaces typos, impossible values, and stray text in number columns. - Count blanks per column (COUNTBLANK). Count exact duplicate rows. - Note every problem in a list before fixing anything. The list becomes the cleaning log. ## Fix, one problem type at a time Work on a copy tab called `clean`. For each fix, they decide, you explain the trade-off: 1. Types: dates parsed as dates, numbers stripped of units and commas, one format per column. Text-that-should-be-number is the most common breakage; show a formula failing on it first. 2. Inconsistent l