This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.
- Set up a retail sales database: Create and populate a retail sales database with the provided sales data.
- Data Cleaning: Identify and remove any records with missing or null values.
- Exploratory Data Analysis (EDA): Perform basic exploratory data analysis to understand the dataset.
- Business Analysis: Use SQL to answer specific business questions and derive insights from the sales data.
The dataset contains transaction records of a retail store, including:
| ColumnName | Description |
|---|---|
| Transaction_ID | Unique transaction identifier |
| Sale_Date | Date of transaction |
| Sale_Time | Time of transaction |
| Customer_Id | Unique Customer identifier |
| Gender | Male Or Female |
| Age | Age of customer |
| Category | Product Category |
| Quantity | Number Of Units Sold |
| Price_Per_Unit | Selling Price of a Single Unit |
| COGS | Cost of Goods Sold |
| Total_Sale | Total revenue per order |
- Customer Demographics: The dataset includes customers from various age groups, with sales distributed across different categories such as Clothing and Beauty.
- High-Value Transactions: Several transactions had a total sale amount greater than 1000, indicating premium purchases.
- Sales Trends: Monthly analysis shows variations in sales, helping identify peak seasons.
- Customer Insights: The analysis identifies the top-spending customers and the most popular product categories.
- Sales Summary: A detailed report summarizing total sales, customer demographics, and category performance.
- Trend Analysis: Insights into sales trends across different months and shifts.
- Customer Insights: Reports on top customers and unique customer counts per category.
This project serves as a comprehensive introduction to SQL for data analysts, covering database setup, data cleaning, exploratory data analysis, and business-driven SQL queries. The findings from this project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.
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Want to contribute? Feel free to fork the repository and explore new insights! 🚀