Project Description
This project is an in-depth analysis of coffee shop sales data using SQL and Power BI. The project involved writing various SQL queries to extract important sales insights and then visualizing those insights in Power BI to create a comprehensive dashboard for better decision-making.
Key SQL Operations:
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Total Sales, Total Orders, Total Quantity Sold: Using aggregation functions to calculate sales, order counts, and quantities for specific periods.
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Sales by Store Location: Identifying performance across different store locations, comparing the total sales for each.
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Weekday vs Weekend Sales: Analyzing sales trends based on the day of the week, specifically comparing weekend vs weekday sales.
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Month-to-Month Sales Trends: Generating insights on how sales fluctuate over different months, helping identify growth or decline.
This project combines the power of SQL for data analysis and Power BI for clear, actionable visual insights, creating a data-driven approach to optimizing coffee shop sales performance. The entire project was guided by a YouTube tutorial, helping me enhance both my SQL skills and my ability to deliver interactive, insightful dashboards.
GitHub Link: Coffee Sales Project Repository
Why I Built This Project
I built this Coffee Sales Analysis Project to develop a deeper understanding of how businesses can leverage data to make informed decisions. Specifically, I wanted to explore how sales data could be analyzed to uncover trends, patterns, and opportunities for optimization in the retail sector.
Ultimately, I built this project to enhance my skills as a data analyst, particularly in SQL and data visualization, and to explore how businesses can use data to grow and thrive.
Project Detail
| Project Detail | Description |
|---|---|
| Date | July 2024 |
| Type | Guided Project- YouTube |
| Tech Stack | PostgreSQL, Power BI |
| Data Source | Coffee Sales Dataset |
| Power BI Insights | - Total sales, quantity sold, and total orders by month, day of the week, store location - Sales trends by product category and time of day - Month-over-month sales growth - Breakdown of weekend vs weekday sales |