Interview Questions Data Analysis: Real Examples with Solutions (2025 Guide)
Breaking into the world of data analysis requires not just technical skills but also the ability to communicate insights clearly. Recruiters and hiring managers often test candidates with a mix of technical interview questions, scenario-based problems, and case studies to evaluate both knowledge and problem-solving ability.
In this guide, I’ll walk you through the most common interview questions data analysis candidates face - complete with detailed solutions, explanations, and examples. Whether you’re preparing for your first role or brushing up before an advanced analytics interview, these questions will help you feel confident.
📌 Table of Contents
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Basic Data Analysis Interview Questions
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SQL and Database-Related Questions
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Excel and Spreadsheet Questions
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Statistics and Probability Questions
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Case Study: Real-World Data Analysis Problem
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Data Visualization and Storytelling Questions
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Business-Oriented Data Analysis Interview Questions
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Advanced Questions on Tools & Techniques
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Final Tips for Cracking Data Analysis Interviews
1. Basic Data Analysis Interview Questions
Q1: What is data analysis?
Answer: Data analysis is the process of inspecting, cleaning, transforming, and interpreting data to discover useful insights, support decision-making, and solve business problems.
Q2: What are the key steps in data analysis?
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Data Collection
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Data Cleaning
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Exploratory Data Analysis (EDA)
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Hypothesis Testing
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Visualization & Reporting
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Decision Making
Tip for interviews: Always explain with an example, e.g., “I analyzed customer churn data by first cleaning missing values, running descriptive statistics, and then building a churn prediction model.”
2. SQL and Database-Related Questions
Q3: Write a SQL query to find the second highest salary in a table of employees.
SELECT MAX(salary) AS second_highest_salary FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);
Q4: How would you find duplicate rows in a dataset?
SELECT employee_id, COUNT(*) FROM employees GROUP BY employee_id HAVING COUNT(*) > 1;
Q5: Real-World Example:
You are asked to find the top 5 customers by purchase amount last month.
SELECT customer_id, SUM(amount) AS total_spent FROM transactions WHERE MONTH(transaction_date) = 7 GROUP BY customer_id ORDER BY total_spent DESC LIMIT 5;
SQL questions are mandatory in almost every data analysis interview.
3. Excel and Spreadsheet Questions
Q6: What functions in Excel do you frequently use for analysis?
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VLOOKUP / XLOOKUP → joining data
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Pivot Tables → summarizing data
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IF / Nested IF → conditional logic
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INDEX + MATCH → advanced lookups
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SUMIFS / COUNTIFS → aggregations with conditions
Q7: How do you handle missing data in Excel?
Options include:
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Replace with mean/median
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Use interpolation
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Flag missing data for deeper review
Q8: Example Excel Case:
You have monthly sales data with missing values. How would you handle it?
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Step 1: Identify missing cells.
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Step 2: Replace missing sales with 3-month rolling average.
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Step 3: Visualize with a line chart to confirm trends are intact.
4. Statistics and Probability Questions
Q9: What is the difference between correlation and causation?
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Correlation = two variables move together (e.g., ice cream sales & temperature).
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Causation = one variable directly impacts another (e.g., vaccination → lower infection rate).
Q10: Explain p-value in hypothesis testing.
The p-value is the probability of observing results as extreme as the current data, assuming the null hypothesis is true.
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p < 0.05 → reject null hypothesis (statistically significant).
Q11: Probability Puzzle:
If you roll two dice, what is the probability that the sum is 7?
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Total outcomes = 36
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Favorable outcomes = 6 (
(1,6), (2,5), (3,4), (4,3), (5,2), (6,1)) -
Probability = 6/36 = 1/6
5. Case Study: Real-World Data Analysis Problem
Case: You are given a dataset of Careem rides with the following fields:
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Ride ID
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Driver ID
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Pickup location
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Drop-off location
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Trip duration
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Trip cost
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Customer rating
Task: Find insights to improve driver efficiency and reduce cancellations.
Approach:
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Data Cleaning → remove duplicates, handle missing ratings.
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Exploratory Analysis:
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Identify average trip durations by location.
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Detect high cancellation zones.
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Compare driver performance.
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Insights:
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Cancellations higher when pickup distance > 5 km.
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Peak traffic hours show longest durations.
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Low-rated drivers correlated with higher cancellations.
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Recommendations:
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Improve driver-customer matching based on distance.
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Introduce surge pricing adjustments for peak times.
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Provide training/incentives for low-rated drivers.
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Case studies like this test business impact + data storytelling.
6. Data Visualization and Storytelling Questions
Q12: What visualization would you use for showing customer churn over time?
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Line chart (trend analysis).
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Cohort analysis (for retention over months).
Q13: How do you choose the right chart?
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Compare categories → bar chart.
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Show distribution → histogram/boxplot.
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Trends → line chart.
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Relationships → scatter plot.
Hiring managers look for clarity: “Can you explain insights to a non-technical stakeholder?”
7. Business-Oriented Data Analysis Interview Questions
Q14: Suppose revenue has dropped by 20% this month. How will you investigate?
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Check seasonality & external factors.
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Break revenue into metrics: customers × average order value.
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Identify whether drop is due to fewer customers or lower spend.
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Deep-dive into segments (region, channel, product line).
Q15: Example Answer:
“In one role, sales dropped in Q2. I broke down by channel and saw online sales were stable but retail outlets had a sharp decline. Further investigation showed a supply chain delay. This helped the business act quickly.”
Always link answers to structured frameworks like AARRR (Acquisition, Activation, Retention, Revenue, Referral).
8. Advanced Questions on Tools & Techniques
Q16: What is A/B Testing?
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Randomized experiment comparing two versions (A = control, B = treatment).
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Used to test product changes, marketing campaigns, pricing.
Q17: How do you detect outliers in data?
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Statistical methods: Z-score, IQR.
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Visualization: Boxplots, scatterplots.
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Machine learning: Isolation Forest, DBSCAN.
Q18: What is the difference between supervised and unsupervised learning?
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Supervised: labeled data (classification, regression).
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Unsupervised: unlabeled data (clustering, dimensionality reduction).
9. Final Tips for Cracking Data Analysis Interviews
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Practice SQL & Excel daily → most common skills tested.
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Revise probability & statistics → often underestimated.
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Work on case studies → practice datasets on Kaggle.
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Learn to explain insights simply → strong communication often differentiates candidates.
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Mock interviews → simulate the real environment.
📌 FAQs on Data Analysis Interview Questions
Q: Are data analysis interview questions mostly technical?
A: Not always - expect a mix of technical (SQL, Excel, statistics) and business-oriented case studies.
Q: How many SQL questions should I prepare for a data analyst interview?
A: At least 20-30 common ones (joins, window functions, aggregations).
Q: Which companies ask data analysis interview questions like these?
A: Tech firms (Careem, Noon, Amazon), banks, consultancies, and FMCG companies across UAE, India, and globally.
📝 Conclusion
Preparing for interview questions data analysis requires a structured approach:
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Master the fundamentals of SQL, Excel, and statistics.
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Practice solving real business case studies.
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Learn to communicate insights through visuals and storytelling.
If you work through the examples above, you’ll be in a strong position to ace your next data analysis interview in 2025.
