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Top 20 #SQL INTERVIEW QUESTIONS
1️⃣ Explain Order of Execution of SQL query
2️⃣ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle )
3️⃣ Write a query to find the cumulative sum/Running Total
4️⃣ Find the Most selling product by sales/ highest Salary of employees
5️⃣ Write a query to find the 2nd/nth highest Salary of employees
6️⃣ Difference between union vs union all
7️⃣ Identify if there any duplicates in a table
8️⃣ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9️⃣ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1️⃣ 0️⃣ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1️⃣ 1️⃣ Write a query to find the Running Difference (Ideal sol'n using windows function)
1️⃣ 2️⃣ Write a query to display year on year/month on month growth
1️⃣ 3️⃣ Write a query to find rolling average of daily sign-ups
1️⃣ 4️⃣ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function)
1️⃣ 5️⃣ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1️⃣6️⃣ Differentiate between a clustered index and a non-clustered index?
1️⃣7️⃣ What is a Candidate key?
1️⃣8️⃣What is difference between Primary key and Unique key?
1️⃣9️⃣What's the difference between RANK & DENSE_RANK in SQL?
2️⃣0️⃣ Whats the difference between LAG & LEAD in SQL?
Access SQL Learning Series for Free: /channel/sqlspecialist/523
Hope it helps :)
Roadmap to become Data Scientist
Читать полностью…
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗔𝗰𝗿𝗼𝘀𝘀 𝗜𝗻𝗱𝗶𝗮 | 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 😍
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How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
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🔰 Python Question / Quiz
What is the output of the following Python code?
𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗙𝗿𝗲𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀)😍
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Data Science vs. Data Analytics
Читать полностью…
Cold email template for Freshers 👇
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship description and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 😍
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𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 - 𝗙𝘂𝗹𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Let’s break them down with clarity:
𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises
---
🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍
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Let’s be honest:
SQL seems simple… until JOINs, Subqueries, and Window Functions come crashing in.
But mastering SQL doesn’t have to be hard.
You just need the right roadmap—and that’s exactly what this is.
Here’s a 5-step SQL journey to go from beginner to job-ready analyst👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Nail the Basics (Learn to Think in SQL)
Start with the foundations:
✅ SELECT, WHERE, ORDER BY
✅ DISTINCT, LIMIT, BETWEEN, LIKE
✅ COUNT, SUM, AVG, MIN, MAX
Practice with small tables to build confidence.
Use platforms like:
➡️ W3Schools
➡️ Modesql
➡️ LeetCode (easy problems)
🔹 𝗦𝘁𝗲𝗽 𝟮: Understand GROUP BY and Aggregations (The Analyst’s Superpower)
This is where real-world queries begin. Learn:
✅ GROUP BY + HAVING
✅ Combining GROUP BY with COUNT/AVG
✅ Filtering aggregated data
Example:
"Find top 5 cities with the highest total sales in 2023"
That’s GROUP BY magic.
🔹 𝗦𝘁𝗲𝗽 𝟯: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But they’re just pattern-matching across tables.
Learn one by one:
✅ INNER JOIN
✅ LEFT JOIN
✅ RIGHT JOIN
✅ FULL OUTER JOIN
✅ SELF JOIN
✅ CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tables—it helps!
🔹 𝗦𝘁𝗲𝗽 𝟰: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)
✅ Subqueries: Query inside another query
✅ CTEs (WITH clause): Cleaner and reusable queries
✅ Use them to break down complex problems
CTEs = the secret sauce to writing queries recruiters love.
🔹 𝗦𝘁𝗲𝗽 𝟱: Level Up with Window Functions (Your Entry into Advanced SQL)
If you want to stand out, this is it:
✅ ROW_NUMBER(), RANK(), DENSE_RANK()
✅ LAG(), LEAD(), NTILE()
✅ PARTITION BY and ORDER BY combo
Use these to:
➡️ Find top N per group
➡️ Track user behavior over time
➡️ Do cohort analysis
You don’t need 100 LeetCode problems.
You need 10 real-world queries done deeply.
Keep it simple. Keep it useful.
SQL Checklist for Data Analysts 🚀
🌱 Getting Started with SQL
👉 Install SQL database software (MySQL, PostgreSQL, or SQL Server)
👉 Set up your database environment and connect to your data
🔍 Load & Explore Data
👉 Understand tables, rows, and columns
👉 Use SELECT to retrieve data and LIMIT to get a sample view
👉 Explore schema and table structure with DESCRIBE or SHOW COLUMNS
🧹 Data Filtering Essentials
👉 Filter data using WHERE clauses
👉 Use comparison operators (=, >, <) and logical operators (AND, OR)
👉 Handle NULL values with IS NULL and IS NOT NULL
🔄 Transforming Data
👉 Sort data with ORDER BY
👉 Create calculated columns with AS and use arithmetic operators (+, -, *, /)
👉 Use CASE WHEN for conditional expressions
📊 Aggregation & Grouping
👉 Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
👉 Group data with GROUP BY and filter groups with HAVING
🔗 Mastering Joins
👉 Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
👉 Understand primary and foreign keys to create meaningful joins
👉 Use SELF JOIN for analyzing data within the same table
📅 Date & Time Data
👉 Convert dates and extract parts (year, month, day) with EXTRACT
👉 Perform time-based analysis using DATEDIFF and date functions
📈 Quick Exploratory Analysis
👉 Calculate statistics to understand data distributions
👉 Use GROUP BY with aggregation for category-based analysis
📉 Basic Data Visualizations (Optional)
👉 Integrate SQL with visualization tools (Power BI, Tableau)
👉 Create charts directly in SQL with certain extensions (like MySQL's built-in charts)
💪 Advanced Query Handling
👉 Master subqueries and nested queries
👉 Use WITH (Common Table Expressions) for complex queries
👉 Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)
🚀 Optimize for Performance
👉 Index critical columns for faster querying
👉 Analyze query plans and use optimizations
👉 Limit result sets and avoid excessive joins for efficiency
📂 Practice Projects
👉 Use real datasets to perform SQL analysis
👉 Create a portfolio with case studies and projects
Here you can find SQL Interview Resources👇
/channel/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: /channel/sqlspecialist
Hope it helps :)
SQL Basics for Beginners: Must-Know Concepts
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).
3. SQL Data Types
Databases store data in different formats. The most common data types are:
- INT (Integer): For whole numbers.
- VARCHAR(n) or TEXT: For storing text data.
- DATE: For dates.
- DECIMAL: For precise decimal values, often used in financial calculations.
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
SELECT column1, column2 FROM table_name;
SELECT * FROM table_name WHERE condition;
ASC) or descending (DESC) order.SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
SELECT * FROM table_name LIMIT 5;
WHERE clause helps you filter data based on a condition:SELECT * FROM employees WHERE salary > 50000;
=: Equal to>: Greater than<: Less thanLIKE: For pattern matchingSELECT COUNT(*) FROM table_name;
SELECT SUM(salary) FROM employees;
SELECT AVG(salary) FROM employees;
SELECT department, AVG(salary) FROM employees GROUP BY department;
SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;
SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;
INSERT INTO statement: INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
UPDATE statement:UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
DELETE statement:DELETE FROM employees WHERE name = 'John Doe';
Data Scientist Roadmap 📈
📂 Python Basics
∟📂 Numpy & Pandas
∟📂 Data Cleaning
∟📂 Data Visualization (Seaborn, Plotly)
∟📂 Statistics & Probability
∟📂 Machine Learning (Sklearn)
∟📂 Deep Learning (TensorFlow / PyTorch)
∟📂 Model Deployment
∟📂 Real-World Projects
∟✅ Apply for Data Science Roles
React "❤️" For More
𝗕𝗶𝗴 𝟰 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 – 𝗔𝗻𝘀𝘄𝗲𝗿 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼!😍
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a = "10" → Variable a is assigned the string "10".
b = a → Variable b also holds the string "10" (but it's not used afterward).
a = a * 2 → Since a is a string, multiplying it by an integer results in string repetition.
"10" * 2 results in "1010"
print(a) → prints the new value of a, which is "1010".
✅ Correct answer: D. 1010
Convolutional Neural Network Cheat Sheet
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Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts ☑️
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
4. Loading Datasets in Python
• Use libraries like pandas for structured data:
import pandas as pd
df = pd.read_csv('data.csv')
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
print(df.shape)
print(df.head())
print(df.isnull().sum())
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𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:
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1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
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