Development of a Self-Learning Educational Tool for Data Analytics Algorithms

The demand for data literacy continues to rise across industries, the need for hands-on, interactive learning tools in education has become increasingly important. This project presents a Data Analytics Self-Learning Educational Tool that bridges the gap between theoretical learning and practical application. The platform is designed for students and aspiring analysts to understand and interact with core data analytics algorithms including classification, clustering, regression, and statistical operations like average, sum, and nearest value. Built using web technologies such as HTML, JavaScript, and ASP.NET, the system supports real-time data processing and visualization. The platform features preloaded examples and allows users to upload their own datasets, providing personalized and scalable learning. It includes step-by-step algorithm demonstrations and graphical outputs that promote critical thinking and experimentation. The backend handles file input, logic processing, and result rendering, while the frontend ensures accessibility and responsiveness across devices. The system has been tested with students and educators to ensure usability, performance, and educational effectiveness. Overall, the tool serves as a dynamic, self-paced environment that fosters algorithmic understanding and data problem-solving skills. The paper elaborates on the design phases, implementation, challenges, and educational impact of the tool.