SQL Programming

SQL Programming

5/14/2025, 3:51:05 AM

*Correct Answer: B) Total number of rows, total revenue, and average price.* COUNT(*) → counts all rows in the sales table. SUM(price * quantity_sold) → calculates total revenue. AVG(price) → gives the average price across all rows. *React ❤️ if you got it right*

SQL Programming

5/13/2025, 6:46:39 PM

You have a table called sales with the following columns: product, price, and quantity_sold. What will this query return? SELECT COUNT(*), SUM(price * quantity_sold), AVG(price) FROM sales;

SQL Programming

5/14/2025, 6:13:02 PM

*Correct Answer: B) Total number of orders placed in each region* COUNT(*) counts the number of rows, i.e., orders. Grouping by region ensures we get one count per region. This is a very common query to measure regional performance or activity. *React ❤️ if you got it right*

SQL Programming

5/14/2025, 1:56:53 PM

Now that you know how to use aggregate functions like SUM(), AVG(), etc., let’s talk about how to use them per category, per product, or per customer using *GROUP BY*. *Why GROUP BY?* Use GROUP BY when you want to aggregate data separately for each value in a column. *Example: Total Sales per Product* SELECT product, SUM(quantity_sold) AS total_quantity FROM sales GROUP BY product; This query shows how many units of each product were sold. Without GROUP BY, you'd only get one total for all products combined. *Another Example: Revenue per Region* SELECT region, SUM(price * quantity_sold) AS revenue FROM sales GROUP BY region; Useful for business insights like which region brings the most revenue. *Golden Rule:* - Every column in the SELECT clause (that’s not inside an aggregate function) must be in the GROUP BY. So this is valid: 👇 SELECT customer_id, COUNT(*) FROM orders GROUP BY customer_id; But this will throw an error: 👇 SELECT customer_id, COUNT(*), status FROM orders GROUP BY customer_id; -- ❌ 'status' is not aggregated or grouped *React with ❤️ if you're ready for the next quiz.* 📝 SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

SQL Programming

5/13/2025, 2:23:44 PM

*Correct Answer: B) It multiplies unit_price and quantity and shows the result in a column called total_sales.* unit_price * quantity performs the arithmetic. AS total_sales renames the result column. So for each row, you’ll get: The product name Its total sales amount (unit_price × quantity) *React ❤️ if you got it right*

SQL Programming

5/14/2025, 4:37:33 PM

Given a table orders with columns: customer_id, order_amount, and region. What does the following query return? SELECT region, COUNT(*) AS total_orders FROM orders GROUP BY region;

SQL Programming

5/16/2025, 5:40:09 PM

Which of the following is true about WHERE and HAVING?

SQL Programming

5/16/2025, 12:38:11 PM

Now, let's move to the next topic in the SQL Learning Series: *HAVING vs WHERE* Both WHERE and HAVING are used to filter data, but they’re used at different stages in a query. Let’s break it down simply: *1. WHERE — Filters Before Grouping* Used to filter individual rows before any grouping or aggregation happens. Example: SELECT region, SUM(order_amount) FROM orders WHERE region != 'South' GROUP BY region; Excludes the 'South' region before grouping and summing. *2. HAVING — Filters After Grouping* Used to filter groups after aggregation. Example: SELECT region, SUM(order_amount) AS total_sales FROM orders GROUP BY region HAVING SUM(order_amount) > 10000; First, it groups orders by region, then only shows regions where total sales are greater than 10,000. Think of it Like This: - WHERE filters rows - HAVING filters groups *Common Mistake:* Using HAVING instead of WHERE when no aggregation is involved. *Wrong:* SELECT * FROM orders HAVING order_amount > 500; -- ❌ *Correct:* SELECT * FROM orders WHERE order_amount > 500; -- ✅ *React with ❤️ if you're ready for the next quiz.* 📝 SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

SQL Programming

5/13/2025, 5:32:57 PM

Let's now move to the next topic in the SQL Learning Series *Aggregations & Grouping!* Aggregate functions can crunch numbers across multiple rows to give a single output (or per group. Let's discussion widely used aggregate functions: *1. COUNT()* Counts the number of non-null rows. SELECT COUNT(*) FROM orders; -- Total number of rows (orders) *2. SUM()* Adds up values in a column. SELECT SUM(amount) FROM orders; -- Total revenue *3. AVG()* Calculates the average. SELECT AVG(rating) FROM reviews; -- Average product rating *4. MIN() / MAX()* Finds smallest or largest value. SELECT MIN(price), MAX(price) FROM products; -- Lowest and highest priced items *Real-Life Example:* SELECT COUNT(*) AS total_orders, SUM(amount) AS total_revenue, AVG(amount) AS avg_order_value FROM orders; *What it does:* - COUNT(*) counts all the orders placed. - SUM(amount) adds up all the money earned — total revenue. - AVG(amount) calculates the average value of an order. This query is often used by analysts or business teams to quickly get a performance snapshot — like a mini dashboard in a single SQL statement. *React with ❤️ if you're ready for the next quiz.* 📝 SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

SQL Programming

5/12/2025, 7:20:52 PM

You have a table sales with columns: product_name, unit_price, quantity. What will this query return? SELECT product_name, unit_price * quantity AS total_sales FROM sales;