You receive a new, messy dataset for an analysis. What are your first steps before any modelling?

What the interviewer wants to hear

• Understand the source, grain and meaning of each column
• Check row counts, duplicates and key uniqueness
• Profile missing values, ranges and outliers
• Check data types, units and date ranges
• Document assumptions and confirm odd findings with data owners

Model answer

First I find out what one row represents and where the data came from, because the grain decides how everything else is analysed. Then I check basic shape: row counts against what I expect, duplicate rows, and whether the supposed keys are really unique. Next I profile every column: data types, share of missing values, ranges, distinct values and obvious outliers such as negative ages or prices of zero. I check dates cover the period I expect and that there are no gaps or sudden jumps that suggest a pipeline change. I also look at units and currencies. Anything strange I write down and confirm with the data owner rather than guessing. Only after that do I decide how to handle missing values and outliers, and I keep the cleaning steps in code so the analysis is reproducible.

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