SQL Data Analysis Project

Overview

This project demonstrates fundamental SQL skills using the Classic Models sample database. The analysis covers data retrieval, filtering, sorting, aggregation, joins, subqueries, and views to extract meaningful business insights from an e-commerce dataset.

Dataset

The project uses the Classic Models Database, a sample database containing information about:

• Customers
• Orders
• Products
• Product Lines
• Payments
• Employees
• Offices

SQL Concepts Demonstrated

Query 1 - Basic SELECT

Retrieved customer names and countries from the customers table.

Query 2 - ORDER BY

Sorted products by purchase price in ascending order.

Query 3 - ORDER BY DESC

Retrieved products sorted by purchase price in descending order.

Query 4 - WHERE Clause

Filtered customers located in the USA.

Query 5 - GROUP BY

Grouped customers by country and calculated customer counts.

Query 6 - Aggregate Function

Used COUNT() to calculate total orders.

Query 7 - INNER JOIN

Combined customer and order information using JOIN operations.

Query 8 - AVG Function

Calculated average customer credit limit.

Query 9 - Subquery

Identified customers whose credit limit exceeds the average credit limit.

Query 10 - VIEW

Created a reusable view combining customer and order information.

Screenshots

The screenshots folder contains execution results for all SQL queries.

Tools Used

• MySQL Server
• MySQL Workbench
• Visual Studio Code
• SQL

Key Skills Demonstrated

• Data Retrieval
• Data Filtering
• Sorting
• Aggregation
• Grouping
• Joins
• Subqueries
• Views
• Business Data Analysis

Project Structure

SQL-Data-Analysis/

├── dataset/

│ └── mysqlsampledatabase.sql

├── screenshots/

│ ├── query1_select.png

│ ├── query2_orderby.png

│ ├── query3_orderby_desc.png

│ ├── query4_where.png

│ ├── query5_groupby.png

│ ├── query6_aggregate.png

│ ├── query7_join.png

│ ├── query8_avg.png

│ ├── query9_subquery.png

│ └── query10_view.png

├── queries.sql

└── README.md

Author

Vaibhav Barman

LinkedIn: https://www.linkedin.com/in/vaibhavbarman

GitHub: https://github.com/vaibhav-barman