<![CDATA[The development of information technology has transformed the way statistical data is managed, analyzed, and presented, moving from static tables to interactive web-based visualizations. Interactive data presentation is considered to enhance users' ability to explore information in greater depth than static descriptive presentations. However, achieving interactive visualization is not simple, as it requires thorough dataset management, from data cleaning and validation to data transformation, as well as appropriate analysis methods to ensure the results are truly informative and easy to interpret. This article sequentially discusses these three main aspects: dataset management, data analysis methods, and interactive visualization approaches, as well as a case study of developing a web-based dashboard using Python and Streamlit. The discussion includes the data cleaning and preparation process, analysis techniques used to identify patterns and trends, and the implementation of interactive components such as dynamic charts, filters, and navigation elements on the dashboard interface. The results of this approach demonstrate that the combination of systematic data management with web visualization can produce information presentations that are more communicative, easily accessible, and support users' independent data exploration.]]>
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