🛠 Tools Used: MySQL
📅 Project Date: May 2025
📁 Skills Demonstrated: SQL Joins, Window Functions, Aggregations, EDA
📦 Project Overview
In this project, I used SQL to explore and analyze a fictional retail store’s sales data. The goal was to answer key business questions around product performance, monthly revenue trends, and top-selling categories using structured queries.
The project simulates how a retail business would use data to drive decisions like stock planning and promotional targeting.
🔍 Business Questions Answered
What are the monthly sales trends?
Which products and categories generate the most revenue?
What’s the average order value?
How do different customer segments behave?
🔧 Key SQL Concepts Used
INNER JOIN,LEFT JOINGROUP BY,ORDER BY,HAVING- Window functions:
ROW_NUMBER(),RANK() CASE WHENfor conditional analysis- Aggregate functions:
SUM(),AVG(),COUNT()
🧠 Insights Generated
💰 The top 10 products contributed over 40% of total sales
📈 Monthly sales peaked in Q4, suggesting seasonal demand
🛒 Average order value: ₹3,200
🧍 Most purchases were made by customers aged 25–35
📄 Sample Query Snippet
SELECT product_name, SUM(total_amount) AS revenue
FROM sales
JOIN
products ON sales.product_id = products.id
GROUP BY product_name
ORDER BY revenue DESC
LIMIT 10;
📌 Deliverables
10+ optimized SQL queries
Result tables exported to CSV
Final report with business insights
🔗 GitHub Repository
🙋 About Me
I’m Satyendra Yadav – a Data Analyst with a background in teaching mathematics and a passion for uncovering stories behind the numbers. I specialize in SQL, Power BI, Excel, and Python to solve real-world problems through data.