✅ Data Analyst Interview Questions for Freshers 📊

1) What is the role of a data analyst?
Answer: A data analyst collects, processes, and performs statistical analyses on data to provide actionable insights that support business decision-making.

2) What are the key skills required for a data analyst?
Answer: Strong skills in SQL, Excel, data visualization tools (like Tableau or Power BI), statistical analysis, and problem-solving abilities are essential.

3) What is data cleaning?
Answer: Data cleaning involves identifying and correcting inaccuracies, inconsistencies, or missing values in datasets to improve data quality.

4) What is the difference between structured and unstructured data?
Answer: Structured data is organized in rows and columns (e.g., spreadsheets), while unstructured data includes formats like text, images, and videos that lack a predefined structure.

5) What is a KPI?
Answer: KPI stands for Key Performance Indicator, which is a measurable value that demonstrates how effectively a company is achieving its business goals.

6) What tools do you use for data analysis?
Answer: Common tools include Excel, SQL, Python (with libraries like Pandas), R, Tableau, and Power BI.

7) Why is data visualization important?
Answer: Data visualization helps translate complex data into understandable charts and graphs, making it easier for stakeholders to grasp insights and trends.

8) What is a pivot table?
Answer: A pivot table is a feature in Excel that allows you to summarize, analyze, and explore data by reorganizing and grouping it dynamically.

9) What is correlation?
Answer: Correlation measures the statistical relationship between two variables, indicating whether they move together and how strongly.

10) What is a data warehouse?
Answer: A data warehouse is a centralized repository that consolidates data from multiple sources, optimized for querying and analysis.

11) Explain the difference between INNER JOIN and OUTER JOIN in SQL.
Answer: INNER JOIN returns only the matching rows between two tables, while OUTER JOIN returns all matching rows plus unmatched rows from one or both tables, depending on whether it’s LEFT, RIGHT, or FULL OUTER JOIN.

12) What is hypothesis testing?
Answer: Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample to infer that a certain condition holds true for the entire population.

13) What is the difference between mean, median, and mode?
Answer:
⦁ Mean: The average of all numbers.
⦁ Median: The middle value when data is sorted.
⦁ Mode: The most frequently occurring value in a dataset.

14) What is data normalization?
Answer: Normalization is the process of organizing data to reduce redundancy and improve integrity, often by dividing data into related tables.

15) How do you handle missing data?
Answer: Missing data can be handled by removing rows, imputing values (mean, median, mode), or using algorithms that support missing data.

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🗄️

📂 Start Here
∟📂 Install SQL Server / MySQL / SQLite
∟📂 Learn How to Run SQL Queries

📂 SQL Basics
∟📂 What is SQL?
∟📂 Basic SELECT Statements
∟📂 Filtering with WHERE Clause
∟📂 Sorting with ORDER BY
∟📂 Using LIMIT / TOP

📂 Data Manipulation
∟📂 INSERT INTO
∟📂 UPDATE
∟📂 DELETE

📂 Table Management
∟📂 CREATE TABLE
∟📂 ALTER TABLE
∟📂 DROP TABLE

📂 SQL Joins
∟📂 INNER JOIN
∟📂 LEFT JOIN
∟📂 RIGHT JOIN
∟📂 FULL OUTER JOIN

📂 Advanced Queries
∟📂 GROUP BY & HAVING
∟📂 Subqueries
∟📂 Aggregate Functions (COUNT, SUM, AVG)

📂 Practice Projects
∟📌 Build a Simple Library DB
∟📌 Employee Management System
∟📌 Sales Report Analysis

📂 ✅ Move to Next Level (Only After Basics)
∟📂 Learn Indexing & Performance Tuning
∟📂 Stored Procedures & Triggers
∟📂 Database Design & Normalization

Credits: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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🐍

📂 Start Here
∟📂 Install Python & VS Code
∟📂 Learn How to Run Python Files

📂 Python Basics
∟📂 Variables & Data Types
∟📂 Input & Output
∟📂 Operators (Arithmetic, Comparison)
∟📂 if, else, elif
∟📂 for & while loops

📂 Data Structures
∟📂 Lists
∟📂 Tuples
∟📂 Sets
∟📂 Dictionaries

📂 Functions
∟📂 Defining & Calling Functions
∟📂 Arguments & Return Values

📂 Basic File Handling
∟📂 Read & Write to Files (.txt)

📂 Practice Projects
∟📌 Calculator
∟📌 Number Guessing Game
∟📌 To-Do List (store in file)

📂 ✅ Move to Next Level (Only After Basics)
∟📂 Learn Modules & Libraries
∟📂 Small Real-World Scripts

For detailed explanation, join this channel 👇
https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

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🧑‍💼 Interviewer: What’s the difference between DELETE and TRUNCATE?

👨‍💻 Me: Both commands are used to remove data from a table, but they work differently:

🔹 DELETE 
– Removes rows one by one, based on a WHERE condition (optional). 
– Logs each row deletion, so it’s slower. 
– Can be rolled back if used within a transaction. 
– Triggers can fire on deletion.

🔹 TRUNCATE 
– Removes all rows instantly—no WHERE clause allowed. 
– Faster, uses minimal logging. 
– Cannot delete specific rows—it's all or nothing. 
– Usually can’t be rolled back in some databases.

🧪 Example:

-- DELETE only inactive users

DELETE FROM users WHERE status = 'inactive';



-- TRUNCATE entire users table

TRUNCATE TABLE users;

💡 Tip: Use DELETE when you need conditions. Use TRUNCATE for a quick full cleanup.

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✅ SQL Interview Challenge – Filter Top N Records per Group 🧠💾

🧑‍💼 Interviewer: How would you fetch the top 2 highest-paid employees per department?

👨‍💻 Me: Use ROW_NUMBER() with a PARTITION BY clause—it's a window function that numbers rows uniquely within groups, resetting per partition for precise top-N filtering.

🔹 SQL Query:

SELECT *

FROM (

  SELECT name, department, salary,

         ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn

  FROM employees

) AS ranked

WHERE rn 



✔ Why it works:  

– PARTITION BY department resets row numbers (starting at 1) for each dept group, treating them as mini-tables.  

– ORDER BY salary DESC ranks highest first within each partition.  

– WHERE rn 


💡 Pro Tip: Swap to RANK() if ties get equal ranks (e.g., two at #1 means next is #3, but you might get 3 rows); DENSE_RANK() avoids gaps. For big datasets, this scales well in SQL Server or Postgres.



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✅ Top 5 SQL Aggregate Functions with Examples 📊💡

1️⃣ COUNT()
Counts rows or non-null values—use COUNT(*) for total rows, COUNT(column) to skip nulls.
Example:

SELECT COUNT(*) AS total_employees FROM Employees;

Tip: In a 1k-row table, it returns 1k; great for validating data completeness.

2️⃣ SUM()
Adds up numeric values—ignores nulls automatically.
Example:

SELECT SUM(salary) AS total_salary FROM Employees;

Tip: For March orders totaling $60, it sums to 60; pair with WHERE for filtered totals like monthly payroll.

3️⃣ AVG()
Calculates average of numeric values—also skips nulls, divides sum by non-null count.
Example:

SELECT AVG(salary) AS average_salary FROM Employees;

Tip: Two orders at $20/$40 avg to 30; use for trends, like mean salary ~$75k in tech firms.

4️⃣ MAX()
Finds the highest value in a column—works on numbers, dates, strings.
Example:

SELECT MAX(salary) AS highest_salary FROM Employees;

Tip: Max order of $40 in a set; useful for peaks, like top sales $150k.

5️⃣ MIN()
Finds the lowest value in a column—similar to MAX but for mins.
Example:

SELECT MIN(salary) AS lowest_salary FROM Employees;

Tip: Min order of $10; spot outliers, like entry-level pay ~$50k.

Bonus Combo Query:

SELECT COUNT(*) AS total,

       SUM(salary) AS total_pay,

       AVG(salary) AS avg_pay,

       MAX(salary) AS max_pay,

       MIN(salary) AS min_pay

FROM Employees;

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