Published Paper Details:

Effective data analysis is essential for driving strategic planning and optimizing operational efficiency, particularly within dynamic fields such as global public health. This project presents a structured, end-to-end data science framework for the analysis, processing, and visualization of global COVID-19 statistics to derive actionable insights for stakeholders. The methodology encompasses acquiring data from diverse sources, including CSV/Excel files, SQL/NoSQL databases, and web APIs/scraping. Rigorous preprocessing was implemented, including Mean/Median/Mode imputation for missing values, Z-score/IQR outlier detection, and Min-max scaling for normalization The core analysis leverages Python libraries (Pandas for manipulation, NumPy for numerical computation, Matplotlib/Seaborn for EDA) and SQL for efficient data management and targeted retrieval. Finally, interactive dashboards were developed using Microsoft Power BI and Tableau to visualize key pandemic metrics such as confirmed cases, recovery rates, age distribution of cases, and vaccination progress. The resulting framework successfully demonstrates how data analysis transforms raw health data into crucial information for understanding global health patterns and guiding strategic interventions.