How do you handle missing or inconsistent data in a dataset?
A technical question focused on data cleaning and preparation techniques essential for data science and engineering roles.
Why Interviewers Ask This
Real-world data is rarely clean. Interviewers test your practical knowledge of handling data imperfections before modeling. They look for robust strategies that maintain data integrity without introducing bias.
How to Answer This Question
Discuss specific techniques like imputation, deletion, or flagging missing values. Explain how you investigate the cause of inconsistency (e.g., sensor error vs. human input). Mention tools or libraries used for detection and correction.
Key Points to Cover
- Identify patterns of missingness
- Choose appropriate imputation strategy
- Validate data sources
Sample Answer
I first investigate the pattern of missingness to determine if it is random or systematic. For small amounts of random missing data, I might use mean or median imputation. For inconsistencies, I validate against source documentation and correct obvious errors. If data quality is poor, I may exclude those records or flag them for further review to prevent model bias.
Common Mistakes to Avoid
- Deleting all rows with missing values indiscriminately
- Ignoring the root cause of errors
- Not discussing bias implications
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