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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?

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Roadmap to become Data Scientist

𝗛𝗼𝘄 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗢𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱!)🧠 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.

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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

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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;
     

- WHERE Clause: Filters data based on conditions.

     SELECT * FROM table_name WHERE condition;
     

- ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

- LIMIT: Limits the number of rows returned.

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause The WHERE clause helps you filter data based on a condition:

   SELECT * FROM employees WHERE salary > 50000;
   

You can use comparison operators like: - =: Equal to - >: Greater than - <: Less than - LIKE: For pattern matching 6. Aggregating Data SQL provides functions to summarize or aggregate data: - COUNT(): Counts the number of rows.

     SELECT COUNT(*) FROM table_name;
     

- SUM(): Adds up values in a column.

     SELECT SUM(salary) FROM employees;
     

- AVG(): Calculates the average value.

     SELECT AVG(salary) FROM employees;
     

- GROUP BY: Groups rows that have the same values into summary rows.

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL Joins combine data from two or more tables: - INNER JOIN: Retrieves records with matching values in both tables.

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data To add new data to a table, you use the INSERT INTO statement:

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data You can update existing data in a table using the UPDATE statement:

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data To remove data from a table, use the DELETE statement:

    DELETE FROM employees WHERE name = 'John Doe';
    

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Random Module in Python 👆

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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?

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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