SQL Internship Task 3 – SQL Basics

Tools Used

• Google Colab
• SQLite
• Python (Pandas)

Dataset Used

Superstore Sales Dataset (CSV)

Objective

The objective of this task is to practice basic SQL operations such as filtering, sorting, grouping, aggregation, and exporting results using a real-world sales dataset.

SQL Queries Used and Their Meaning

1. Display all records from the sales table

SELECT * FROM sales;

Meaning: This query retrieves all rows and columns from the sales table to understand the dataset.

2. Filter products belonging to the Technology category

SELECT * FROM sales
WHERE Category = 'Technology';

Meaning: This query filters the data and shows only those records where the product category is Technology.

3. Sort all records by Sales in descending order

SELECT * FROM sales
ORDER BY Sales DESC;

Meaning: This query sorts the data from highest to lowest sales.

4. Calculate total sales for each category

SELECT Category, SUM(Sales) AS Total_Sales
FROM sales
GROUP BY Category;

Meaning: This query groups the data by category and calculates total sales for each category.

5. Count number of orders in each region

SELECT Region, COUNT(*) AS Total_Orders
FROM sales
GROUP BY Region;

Meaning: This query counts how many orders were placed in each region.

6. Show categories where total sales is greater than 100000

SELECT Category, SUM(Sales) AS Total_Sales
FROM sales
GROUP BY Category
HAVING SUM(Sales) > 100000;

Meaning: This query filters grouped results to show only categories with high total sales.

7. Find customers whose name starts with the letter A

SELECT * FROM sales
WHERE "Customer Name" LIKE 'A%';

Meaning: This query retrieves records where the customer name starts with the letter A.

8. Find top 5 customers by total sales

SELECT "Customer Name", SUM(Sales) AS Total_Spend
FROM sales
GROUP BY "Customer Name"
ORDER BY Total_Spend DESC
LIMIT 5;

Meaning: This query finds the top 5 customers who spent the most.

Exported Output

• sales_summary.csv contains category-wise total sales.

Outcome

This task helped me understand how to use SQL for filtering, sorting, grouping, and analyzing real-world sales data.