Sales Data Analysis and Visualization Using Python, SQL, Excel, Power BI, and Streamlit

In the era of data-driven decision-making, organiza-tions rely heavily on analytical systems to extract meaningful in-sights from large volumes of business data. Traditional reporting methods, which are often static and manual, fail to provide timely insights and lack the flexibility required for dynamic analysis. This paper presents a comprehensive Sales Data Analytics System that integrates Microsoft Excel, Python, SQL, Power BI, and Streamlit into a unified analytical framework. The system is designed to transform raw transactional data into structured, meaningful, and interactive insights through data preprocessing, storage, analysis, and visualization. Key performance indicators such as total revenue, net revenue, order count, average order value, category-wise contribution, and salesperson performance are generated and analyzed. The final output is deployed through a Streamlit-based web application, enabling real-time interaction and accessibility. The proposed system improves data accuracy, enhances visualization, and supports efficient business decision-making. The framework is scalable, modular, and suitable for both academic and practical applications in business analytics.