Data Analysis Interview Questions & Answers
The most-asked data analysis interview questions — SQL, Python, statistics and dashboards — with clear, honest answers. Read them, then practise out loud in a free AI mock interview.
Practise these in a free AI mock interview1. What is the difference between INNER JOIN and LEFT JOIN?
INNER JOIN returns only rows that match in both tables. LEFT JOIN returns every row from the left table plus matches from the right, with NULLs where there is no match. Use LEFT JOIN when you must keep all records from the main table even if they have no related row.
2. How do you handle missing values in a dataset?
First understand why they are missing. Then either drop rows/columns (if few and non-critical) or impute — mean/median for numbers, mode or an "Unknown" category for text, forward-fill for time series. Always document your choice, since imputation can bias results.
3. Explain correlation vs causation with an example.
Correlation means two variables move together; causation means one actually drives the other. Ice-cream sales and drownings rise together in summer but neither causes the other. Confusing them leads to wrong business decisions.
4. What is a p-value in simple terms?
It is the probability of seeing your result (or a more extreme one) if there were truly no real effect. A small p-value (say < 0.05) suggests the effect is unlikely to be random chance. It does not tell you the size or importance of the effect.
5. How would you find the top 3 products by revenue per region in SQL?
Use a window function: RANK() OVER (PARTITION BY region ORDER BY revenue DESC), then keep rows where the rank is 3 or less. Window functions rank within each group without collapsing rows like GROUP BY would.
6. What is the difference between WHERE and HAVING?
WHERE filters individual rows before grouping; HAVING filters groups after GROUP BY aggregation. Use WHERE for raw conditions (age > 25) and HAVING for aggregate conditions (SUM(sales) > 1000).
7. When would you use a bar chart vs a line chart?
Bar charts compare values across categories (sales by product). Line charts show a trend over a continuous axis, usually time (revenue by month). Using a line for unordered categories misleads the viewer.
8. Walk me through cleaning a messy CSV in pandas.
Load with read_csv, inspect via info()/describe(), drop exact duplicates, standardise column names, fix data types (dates, numbers), handle missing values, trim/normalise strings, and validate ranges — all in a reproducible script.
9. What is a CTE and why use it?
A Common Table Expression (WITH ... AS) is a named temporary result you reference in the main query. It makes complex queries readable, lets you build them step by step, and is required for recursive queries.
10. How do you explain your analysis to a non-technical manager?
Lead with the decision or insight, not the method. Use one clear chart, plain language, and a concrete recommendation. Keep the technical detail ready for follow-up questions, but do not open with it.
Knowing the answers isn’t enough — say them out loud
Practise these Data Analysis questions in a free AI mock interview: answer by voice, get instant feedback on your strengths and the gaps to fix.
Admissions open · free to apply
Request your admission
Attended a masterclass or have a friend's referral code? You get ₹15,000 off. Fill this and our team takes it from here — pay by cash or online.