When would you use Excel, SQL or Python for an analysis task?
What the interviewer wants to hear
- Excel for small data, quick checks and sharing with business users
- SQL for pulling and aggregating large data in the warehouse
- Python for complex cleaning, statistics, automation and reproducibility
- Often combine them in one workflow
Model answer
I choose based on data size, complexity and who will use the result. Excel is great for small datasets, quick what-if checks, and when a business user needs to play with the numbers themselves. It becomes risky with large data and manual steps that are hard to repeat. SQL is my default for pulling and aggregating data from the warehouse, because the computation happens where the data lives and it handles millions of rows easily. Python with pandas is best when I need complex cleaning, statistical tests, merging many sources, or automating a report that runs every week. It is also more reproducible because the whole process is in code. In practice I often combine them: SQL to extract and aggregate, Python for analysis, and a BI tool or Excel for the final view.
Can you answer it under time pressure?
Take a timed medium round and get scored on every answer.
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