In my project on data analytics for a coffee shop's sales report, I utilized MySQL for data management and Power BI for data visualization to provide comprehensive insights into the shop's sales performance. The project began with extracting and managing essential datasets using MySQL, which included sales transactions, product information, employee records, customer data, and a time dimension. These datasets were meticulously cleaned and transformed to ensure consistency and accuracy, including the creation of calculated fields like revenue. Creating a sales report for a coffee shop using SQL and Power BI involves a few key steps. First, you'll use SQL to extract and manipulate your data. This starts with extracting relevant sales data from your database, including details like transaction IDs, dates, product IDs, product names, quantities, prices, and total amounts. You can then use SQL queries to aggregate and summarize this data, such as calculating total sales, average transaction values, and total quantities sold for each product.

Next, you'll import this data into Power BI, either by connecting directly to your database or by importing CSV files. In Power BI, you can model your data by establishing relationships between different tables (e.g., linking sales data to product and customer information). After that, you can create various visualizations to represent the sales data, such as bar charts for total sales per product, line charts for sales trends over time, and pie charts for sales distribution among different product categories. Finally, you can compile these visualizations into an interactive dashboard, making it easy to analyze and present the coffee shop's sales performance. Subsequently, the cleaned data was imported into Power BI, where I designed an interactive and dynamic sales report. This report featured various visualizations such as bar charts to display daily sales, line graphs for monthly sales trends, and pie charts to show the contribution of each product category to overall sales. Additionally, tables were used to highlight top-selling products and the performance of individual employees. Interactive elements like slicers and filters allowed users to explore the data dynamically, enabling detailed analysis of specific time periods, products, or employee performance.

This project provided the coffee shop's management with real-time, actionable insights into sales trends, product performance, and employee contributions, facilitating informed decision-making and strategic growth. The integration of MySQL and Power BI resulted in a robust data analytics solution that significantly enhanced the shop's operational efficiency and business optimization.