Top 100 Data Analyst Interview Questions & Answers
#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience
Part 1: SQL Questions (Q1-30)
#1. What is the difference between
A:
•
•
•
#2. Select all unique departments from the
A: Use the
#3. Find the top 5 highest-paid employees.
A: Use
#4. What is the difference between
A:
•
•
#5. What are the different types of SQL joins?
A:
•
•
•
•
•
#6. Write a query to find the second-highest salary.
A: Use
#7. Find duplicate emails in a
A: Group by the email column and use
#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.
#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.
#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a
#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience
Part 1: SQL Questions (Q1-30)
#1. What is the difference between
DELETE, TRUNCATE, and DROP?A:
•
DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.•
TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.•
DROP is a DDL command that removes the entire table, including its structure, data, and indexes.#2. Select all unique departments from the
employees table.A: Use the
DISTINCT keyword.SELECT DISTINCT department
FROM employees;
#3. Find the top 5 highest-paid employees.
A: Use
ORDER BY and LIMIT.SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;
#4. What is the difference between
WHERE and HAVING?A:
•
WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).•
HAVING is used to filter groups after aggregations (GROUP BY) have been performed.-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;
#5. What are the different types of SQL joins?
A:
•
(INNER) JOIN: Returns records that have matching values in both tables.•
LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.•
RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.•
FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.•
SELF JOIN: A regular join, but the table is joined with itself.#6. Write a query to find the second-highest salary.
A: Use
OFFSET or a subquery.-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;
-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
#7. Find duplicate emails in a
customers table.A: Group by the email column and use
HAVING to find groups with a count greater than 1.SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;
#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.
#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.
-- Rank employees by salary within each department
SELECT
name,
department,
salary,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;
#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a
SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.❤2
✨Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
📝 Summary:
Gradio is an open-source Python package that creates visual interfaces for ML models, making them accessible to non-specialized users via a URL. This improves collaboration by allowing easy interaction, feedback, and trust-building in interdisciplinary settings.
🔹 Publication Date: Published on Jun 6, 2019
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.02569
• PDF: https://arxiv.org/pdf/1906.02569
• Github: https://github.com/gradio-app/gradio
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• https://huggingface.co/CxECHO/CE
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• https://huggingface.co/datasets/society-ethics/papers
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• https://huggingface.co/spaces/orYx-models/Nudge_Generator
• https://huggingface.co/spaces/society-ethics/about
• https://huggingface.co/spaces/mindmime/gradio
==================================
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📝 Summary:
Gradio is an open-source Python package that creates visual interfaces for ML models, making them accessible to non-specialized users via a URL. This improves collaboration by allowing easy interaction, feedback, and trust-building in interdisciplinary settings.
🔹 Publication Date: Published on Jun 6, 2019
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.02569
• PDF: https://arxiv.org/pdf/1906.02569
• Github: https://github.com/gradio-app/gradio
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• https://huggingface.co/CxECHO/CE
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• https://huggingface.co/datasets/society-ethics/papers
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• https://huggingface.co/spaces/orYx-models/Nudge_Generator
• https://huggingface.co/spaces/society-ethics/about
• https://huggingface.co/spaces/mindmime/gradio
==================================
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arXiv.org
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes...
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📝 Summary:
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🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15440
• PDF: https://arxiv.org/pdf/2601.15440
• Project Page: https://pypi.org/project/dla-ideal-solver/
• Github: https://github.com/sandyherho/dla-ideal-solver
==================================
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📝 Summary:
This paper details a Numba-accelerated Python framework for 2D DLA simulations. It confirms a fractal dimension of 1.71 for dilute regimes but reveals a crossover to 1.87 compact growth in high-density environments. This provides an open-source testbed.
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15440
• PDF: https://arxiv.org/pdf/2601.15440
• Project Page: https://pypi.org/project/dla-ideal-solver/
• Github: https://github.com/sandyherho/dla-ideal-solver
==================================
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