Structure your answer around a systematic, iterative process: start with understanding the data and its context, then profile and assess quality, apply targeted cleaning techniques, validate results, and document everything. Emphasize that cleaning is not just about fixing errors but about ensuring the data is fit for the intended analysis or product use case.
Pro tip: Always tie your cleaning steps back to the business or product impact—e.g., how missing values could skew metrics or how outliers might affect model performance—to show you think beyond just technical fixes.
Clarify the data's source, meaning, and intended use. Identify what 'clean' means for this specific dataset and analysis goal.
Use summary statistics, visualizations, and automated tools to detect missing values, duplicates, outliers, inconsistencies, and formatting issues.
Handle missing data (impute or drop), remove duplicates, fix inconsistencies (e.g., date formats, categorical labels), and treat outliers appropriately.
Re-profile the cleaned data to ensure issues are resolved and no new problems were introduced. Iterate as needed based on validation results.
Record all cleaning steps, assumptions, and transformations for reproducibility. Automate the pipeline where possible for future datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.