Top 50 Interview Questions for Data Analytics Roles

Landing a data analytics role requires both technical skills and the ability to communicate insights effectively. Here are the most commonly asked interview questions with detailed answers.

SQL Questions

1. What is the difference between WHERE and HAVING clauses?

Answer: WHERE filters rows before grouping, while HAVING filters groups after aggregation.

-- WHERE example
SELECT * FROM employees WHERE salary > 50000;

-- HAVING example
SELECT department, AVG(salary)
FROM employees
GROUP BY department
HAVING AVG(salary) > 60000;

2. Explain different types of JOINs

Answer:

• INNER JOIN: Returns matching rows from both tables
• LEFT JOIN: All rows from left table + matching from right
• RIGHT JOIN: All rows from right table + matching from left
• FULL OUTER JOIN: All rows from both tables

3. How do you find duplicate records?

SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

Python/Pandas Questions

4. How do you handle missing data in Pandas?

Answer: Multiple approaches:

# Drop missing values
df.dropna()

# Fill with mean
df.fillna(df.mean())

# Forward fill
df.fillna(method='ffill')

# Custom value
df.fillna(0)

5. Explain the difference between merge() and join()

Answer: Both combine DataFrames, but:

• merge() is more flexible, can merge on columns or indexes
• join() is simpler, typically joins on indexes

Statistics Questions

6. What is the difference between correlation and causation?

Answer: Correlation means variables move together, but causation means one variable directly causes change in another. Correlation doesn't imply causation.

7. Explain Type I and Type II errors

Answer:

• Type I (False Positive): Rejecting a true null hypothesis
• Type II (False Negative): Failing to reject a false null hypothesis

8. What is a p-value?

Answer: The probability of obtaining results at least as extreme as observed, assuming the null hypothesis is true. Typically, p < 0.05 indicates statistical significance.

Data Visualization Questions

9. When would you use a bar chart vs. a pie chart?

Answer:

• Bar chart: Comparing quantities across categories, showing changes over time
• Pie chart: Showing parts of a whole (use sparingly, max 5-6 categories)

10. What makes a good dashboard?

Answer:

• Clear objectives and KPIs
• Visual hierarchy
• Appropriate chart types
• Interactive filters
• Mobile responsiveness
• Fast load times

Business Intelligence Questions

11. Explain OLAP vs OLTP

Answer:

• OLTP: Online Transaction Processing - daily operations, fast queries
• OLAP: Online Analytical Processing - complex analysis, historical data

12. What is ETL?

Answer: Extract, Transform, Load - the process of:

1. Extract: Getting data from various sources
2. Transform: Cleaning and structuring data
3. Load: Storing in data warehouse

Practical Problem-Solving

13. How would you analyze customer churn?

Answer:

1. Define churn (e.g., no activity in 90 days)
2. Identify churned vs active customers
3. Analyze demographics, behavior patterns
4. Build cohort analysis
5. Create predictive model
6. Recommend retention strategies

14. A metric suddenly drops by 20%. How do you investigate?

Answer:

1. Verify data accuracy and collection
2. Check for technical issues
3. Segment the data (geography, device, user type)
4. Compare with same period last year
5. Look for external factors
6. Investigate recent product changes

Advanced Questions

15-20. Machine Learning Basics

Questions about regression, classification, decision trees, random forests, and model evaluation metrics.

21-30. Data Warehousing

Questions about dimensional modeling, star schemas, fact and dimension tables.

31-40. Advanced SQL

Window functions, CTEs, recursive queries, query optimization.

41-50. Business Scenarios

Real-world case studies about A/B testing, customer segmentation, revenue analysis.

How to Prepare

1. Practice SQL daily on LeetCode, HackerRank
2. Work with real datasets from Kaggle
3. Build a portfolio of analysis projects
4. Learn to tell stories with data
5. Understand business context for analytics

Common Interview Structure

1. Introduction (5 min)
2. Technical Questions (30-40 min)
3. Case Study/Take-home (20-30 min)
4. Behavioral Questions (15-20 min)
5. Your Questions (5-10 min)

Red Flags to Avoid

• Not asking clarifying questions
• Jumping to solutions without understanding
• Only focusing on technical skills
• Poor communication of insights
• Not considering business impact

Conclusion

Data analytics interviews test both technical proficiency and business acumen. Practice these questions, work on real projects, and always connect your analysis to business value.

Want structured training for data analytics? Explore our Data Analysis with Python course.