Post #1753

📘SQL Challenges for Data Analytics – With Explanation🧠 (Beginner ➡️ Advanced) 1️⃣Select Specific Columns SELECT name, email FROM users; This fetches only the name and email columns from the users table. ✔️ Used when you don’t want all columns from a table. 2️⃣ Filter Records with WHERE SELECT * FROM users WHERE age > 30; The WHERE clause filters rows where age is greater than 30. ✔️Used for applying conditions on data. 3️⃣ ORDER BY Clause SELECT * FROM users ORDER BY registered_at DESC; Sorts all users based on registered_at in descending order. ✔️Helpful to get latest data first. 4️⃣ Aggregate Functions (COUNT, AVG) SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users; Explanation: - COUNT(*) counts total rows (users). - AVG(age) calculates the average age. ✔️Used for quick stats from tables. 5️⃣ GROUP BY Usage SELECT city, COUNT(*) AS user_count FROM users GROUP BY city; Groups data by city and counts users in each group. ✔️Use when you want grouped summaries. 6️⃣ JOIN Tables SELECT users.name, orders.amount FROM users JOIN orders ON users.id = orders.user_id; Fetches user names along with order amounts by joining users and orders on matching IDs. ✔️Essential when combining data from multiple tables. 7️⃣ Use of HAVING SELECT city, COUNT(*) AS total FROM users GROUP BY city HAVING COUNT(*) > 5; Like WHERE, but used with aggregates. This filters cities with more than 5 users. ✔️ **Use HAVING after GROUP BY.** 8️⃣ Subqueries SELECT * FROM users WHERE salary > (SELECT AVG(salary) FROM users); Finds users whose salary is above the average. The subquery calculates the average salary first. ✔️Nested queries for dynamic filtering9️⃣ CASE Statementnt** SELECT name, CASE WHEN age < 18 THEN 'Teen' WHEN age <= 40 THEN 'Adult' ELSE 'Senior' END AS age_group FROM users; Adds a new column that classifies users into categories based on age. ✔️Powerful for conditional logic. 🔟 Window Functions (Advanced) SELECT name, city, score, RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank FROM users; Ranks users by score *within each city*. SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075