How would you handle 10% nulls in a key column?
This tests your ability to diagnose data issues before solving. First, investigate why data is null. Then, discuss trade-offs of dropping vs. imputing with the mean or median, considering the impact on the dashboard's accuracy.
This tests your ability to diagnose data integrity issues before solving and articulate statistical trade-offs. A great answer starts by investigating *why* 10% of the data is null. Then, it weighs the pros and cons of key options: dropping rows (data loss), imputing with the mean (reduces variance), or using the median (more robust). The final choice should be justified by the data's distribution and the dashboard's goal. A red flag is immediately picking a method without this diagnostic step.
Read the original → scikit-learn.org
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- #analytics
- #data quality
- #statistics
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