Fresher data analyst interviews in India usually follow a pattern: an online test or take-home task, a technical round covering SQL, Excel and statistics, sometimes a short case, and an HR or managerial round. The questions below are grouped the same way. Each has a short model answer you should put in your own words, ideally with an example from your projects. For a longer drill in a single area, use our SQL interview questions and Python interview questions pages.
SQL questions
1. What is the difference between INNER JOIN and LEFT JOIN?
An INNER JOIN returns only rows with matching keys in both tables. A LEFT JOIN returns every row from the left table, with NULLs where the right table has no match. Use a LEFT JOIN when you must not lose records, for example customers who have not ordered yet.
2. What is the difference between WHERE and HAVING?
WHERE filters rows before grouping; HAVING filters groups after aggregation. "Cities with more than 100 orders" needs HAVING COUNT(*) > 100 after GROUP BY city.
3. Why does GROUP BY give an error when I select a column not in the group?
Every selected column must either be in the GROUP BY clause or wrapped in an aggregate like SUM or MAX, because the database cannot know which single value to show for a group.
4. What are window functions? Give an example.
Window functions calculate across a set of related rows without collapsing them. ROW_NUMBER() OVER (PARTITION BY region ORDER BY revenue DESC) ranks products within each region while keeping every row.
5. RANK vs DENSE_RANK vs ROW_NUMBER?
ROW_NUMBER gives unique numbers even for ties. RANK gives ties the same number and then skips (1, 1, 3). DENSE_RANK gives ties the same number without gaps (1, 1, 2).
6. How would you find duplicate records?
GROUP BY the columns that define a duplicate and keep groups HAVING COUNT(*) > 1. To remove them, use ROW_NUMBER() partitioned by those columns and keep only row 1.
7. How do NULLs affect aggregates?
COUNT(column), SUM and AVG ignore NULLs, while COUNT(*) counts all rows. An average can therefore look higher than expected if missing values are silently skipped, so check NULLs first.
Excel questions
8. VLOOKUP vs XLOOKUP?
VLOOKUP searches only the first column of a range and returns a value to its right by column number, so it breaks if columns are inserted. XLOOKUP looks up in any column, returns from any column, defaults to exact match and handles not-found values cleanly.
9. When would you use a pivot table?
To summarise large data quickly, for example sales by month and region, and to slice it by filters. Mention that you refresh the pivot after data changes and keep the source as a proper table.
10. How do you clean a messy Excel sheet?
Remove duplicates, trim extra spaces, fix data types such as dates stored as text, standardise categories ("Bangalore" vs "Bengaluru"), and flag blanks rather than deleting them blindly. Power Query helps for repeatable cleaning.
For Excel practice, the free Excel course covers these functions.
Statistics questions
11. When is the median better than the mean?
When data is skewed or has outliers. Salaries or order values are typical examples; a few very large values drag the mean up while the median stays representative.
12. How do you detect outliers?
Visually with a box plot, or with the IQR rule (values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR). Then investigate before removing: an outlier can be a data error or a genuine important case.
13. What is the difference between correlation and causation?
Correlation means two variables move together; causation means one changes the other. Ice cream sales and AC sales rise together because of summer, not because one causes the other.
14. What is a p-value, in simple words?
Assuming there is no real effect, it is the probability of seeing a result at least as extreme as yours. A small p-value suggests the result is unlikely to be chance alone, but it does not measure how big or important the effect is.
BI tool questions
15. How would you design a sales dashboard?
Start from the audience and the three or four decisions they make. Put headline KPIs at the top, trends in the middle and a detailed table with filters at the bottom. Keep colours consistent and avoid decorative charts.
16. What is the difference between a calculated column and a measure in Power BI?
A calculated column is computed row by row and stored in the model. A measure is computed at query time based on the current filter context, so it is the right choice for totals and ratios that change with slicers.
Case question
17. "Orders dropped 15% last week. How would you investigate?"
Structure your answer. First confirm the data is correct (tracking or pipeline issue?). Then break the drop down by city, channel, device, product category and new vs repeat customers to find where it is concentrated. Check external factors such as holidays, price changes, stock-outs or app releases. Finally, state what you would recommend and what data you would need. Interviewers care more about structure than about the "right" answer.
Behavioural and HR questions
18. Walk me through a project on your resume.
Use a simple arc: the question, the data, what you cleaned, the analysis, the result and what you would do differently. Keep it under two minutes. Our data analyst resume guide shows how to frame projects so this answer is easy.
19. Tell us about a time your analysis was wrong.
Pick a real example, such as a join that duplicated rows, explain how you noticed and what check you now run every time. Owning a mistake and the fix is a strong signal.
20. How do you explain a finding to a non-technical manager?
Lead with the conclusion and the action, then show one chart, and keep method details for questions. For example: "Returns are concentrated in two cities because of late deliveries; fixing courier allocation there is the quickest win."
For opening lines in the HR round, see how to introduce yourself in an interview.
What the first screening round looks like
Before any of these questions, many companies run an online screen: a short aptitude section, a few SQL queries on a sample schema, and sometimes a small Excel or dashboard task to submit within a day or two. Practise under time pressure with the skills assessment and a mock interview, and browse the interview question bank for more variations. The skills tested shift with the role, so read the listing closely: SQL internships lean on query writing, while Power BI internships for freshers and Tableau internships usually ask you to build or critique a dashboard. Role pages such as data analyst intern show what employers in each city are asking for.
A one-week preparation plan
| Day | Focus | Resource |
|---|---|---|
| 1–2 | SQL joins, GROUP BY, window functions | SQL course |
| 3 | Excel lookups, pivots, cleaning | Your own sample dataset |
| 4 | Statistics basics and one A/B example | data science lessons |
| 5 | Rebuild one dashboard from scratch and explain it aloud | Your portfolio |
| 6 | Aptitude and a timed mock | mock placement test, aptitude test |
| 7 | Project walk-throughs and HR answers | interview prep hub |
Once you feel ready, apply to data analyst internships for freshers or the wider data analyst internships list, and revise common general questions in our top internship interview questions guide.
Frequently asked questions
What is asked in a fresher data analyst interview?
Most interviews cover SQL queries, Excel functions such as lookups and pivots, basic statistics, a dashboard or BI discussion, a short business case and questions about your projects. Many companies also run an aptitude or take-home test first.
How much SQL does a fresher data analyst need?
You should be comfortable with joins, GROUP BY with HAVING, subqueries or CTEs, and basic window functions like ROW_NUMBER and RANK. Being able to explain why a query gives a certain result matters as much as writing it.
How do I answer a case question with no business experience?
Use a clear structure: check the data first, break the problem into segments, consider outside factors, then recommend an action. Interviewers mainly want to see organised thinking rather than industry knowledge.
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