10 Data Analyst Interview Questions You Should Be Ready For (2025) 💼📊

#programming #beginners #jupyter #datascience

1️⃣ Q: Explain the difference between INNER JOIN and LEFT JOIN.

✅ INNER JOIN returns only matching records between tables.

✅ LEFT JOIN returns all records from the left table + matched records from the right (NULLs if no match).

Example:

Get all customers and their orders:

INNER JOIN → shows only customers who placed orders.
LEFT JOIN → shows all customers, even those with no orders.

2️⃣ Q: What are window functions in SQL?

Window functions perform calculations across a set of rows related to the current row.

Example:

Use ROW_NUMBER() to rank sales per region without collapsing data:

sql
SELECT region, sales, ROW_NUMBER() OVER(PARTITION BY region ORDER BY sales DESC)

FROM sales_data;

3️⃣ Q: How do you handle missing or duplicate data in a dataset?

For missing data: use imputation (mean/median), drop, or flag it.
For duplicates: use .drop_duplicates() in Python or DISTINCT in SQL. Approach depends on context and business impact.

4️⃣ Q: Describe a situation where you derived insights that influenced a business decision.
Example: Analyzed user engagement data → found drop in mobile retention. Suggested UX improvements → increased retention by 15%.

5️⃣ Q: What’s the difference between correlation and causation?

Correlation = relationship (e.g., A & B move together)
Causation = A directly causes B Always validate with experiments or domain expertise.

6️⃣ Q: How would you optimize a slow SQL query?

Check for missing indexes
Avoid SELECT *
Use EXPLAIN plans
Limit subqueries
Optimize joins and filters

7️⃣ Q: Explain the use of GROUP BY and HAVING in SQL.

GROUP BY aggregates data by columns (e.g., total sales by region)
HAVING filters aggregated results Example: sql SELECT region, SUM(sales) FROM orders GROUP BY region HAVING SUM(sales) > 10000;

8️⃣ Q: How do you choose the right chart for a dataset?