Data Analyst Interview Questions for Freshers (India, 2026): SQL, Excel, Python, Statistics & Case Questions
Data analyst is one of the most popular fresher roles in India — at service companies like TCS, Infosys, Accenture and Deloitte, at analytics firms, and at product startups. Interviewers check four things: can you write SQL without help, can you work in Excel or Python, do you understand basic statistics, and can you turn data into a clear business answer. Here are the question types you will actually face, grouped by topic, with what a good answer covers.
How data analyst interviews work for freshers
- Online assessment — aptitude, SQL or Excel MCQs, and sometimes a short SQL or Python task.
- Technical interview — SQL queries written live, Excel or pandas questions, and basic statistics.
- Case or business round — a metric question, a guesstimate, or a walk-through of a dashboard you built.
- HR round — communication, relocation and why you want an analytics career.
Selection steps and test sections change between hiring drives. Always confirm the current pattern on the company's official careers page or your drive invitation before you prepare.
SQL questions (asked in almost every interview)
- Difference between WHERE and HAVING.
- Explain INNER, LEFT, RIGHT and FULL joins with an example.
- Write a query to find the second highest salary.
- Find duplicate rows in a table and delete them.
- Difference between RANK(), DENSE_RANK() and ROW_NUMBER().
- Write a query for month-wise total sales using GROUP BY.
- What is a subquery vs a CTE? When would you use each?
- Difference between UNION and UNION ALL.
Say the logic out loud before writing the query — which table, which join, which filter. Interviewers score your reasoning, not just the final output.
Excel questions
- Difference between VLOOKUP, HLOOKUP, INDEX-MATCH and XLOOKUP.
- How do you build a pivot table and what can it summarise?
- How do you remove duplicates and handle blank cells?
- Explain IF, COUNTIF, SUMIF and their multi-condition versions.
- What is conditional formatting used for?
Python and pandas questions
- How do you read a CSV file and check its shape, columns and data types?
- How do you handle missing values — drop, fill with mean/median, or flag?
- Explain groupby, merge and pivot_table with an example.
- Difference between loc and iloc.
- How do you find and remove outliers?
- Which charts would you use to show a trend, a comparison and a distribution?
Statistics questions
- Difference between mean, median and mode — when is median better?
- What are variance and standard deviation?
- What is a normal distribution?
- Difference between correlation and causation, with an example.
- What is a p-value and a null hypothesis in simple words?
- What is A/B testing and how would you read the result?
Power BI / Tableau and dashboard questions
- Walk me through a dashboard you built. Who used it and what decision did it support?
- Difference between a measure and a calculated column in Power BI.
- What is DAX? Explain CALCULATE in simple words.
- How do you choose which KPIs to show on a dashboard?
Case and guesstimate questions
- Sales dropped 20% last month. How would you find out why?
- Which metrics would you track for a food delivery app?
- Estimate the number of two-wheelers sold in a city in a year.
- How would you measure whether a new app feature is working?
For case questions, clarify the goal, break the problem into parts (time, region, product, customer segment), say what data you would check, and end with a recommendation.
Project and HR questions
- Explain your analytics project end to end — data source, cleaning, analysis, insight.
- What was the most surprising insight you found?
- Tell me about yourself.
- Why do you want to be a data analyst?
2-week data analyst prep plan
- Days 1–4: SQL — joins, GROUP BY, subqueries, window functions; write 5 queries a day.
- Days 5–6: Excel — lookups, pivot tables, cleaning functions.
- Days 7–8: Python and pandas on one public dataset.
- Day 9: statistics basics and A/B testing in simple words.
- Day 10: build or polish one dashboard you can explain in 2 minutes.
- Day 11: practise 3 case and guesstimate questions out loud.
- Day 12: prepare your project story and 'tell me about yourself'.
- Days 13–14: take full mock interviews out loud and fix the weak answers.
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