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📘 SQL Challenges for Data Analytics – With Explanation 🧠

(Beginner ➡️ Advanced)

1️⃣ Select 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