I applied Data Feminism principles to analyze large-scale U.S. unemployment datasets using Python and its API Seaborn, creating two contrasting narratives from identical economic data. To do this, I processed employment statistics spanning multiple years and demographic categories, implementing data visualization techniques to demonstrate how framing and selection bias influence statistical interpretation.

Used Python for data manipulation and Seaborn for creating visualizations that supported opposing economic narratives—one highlighting recovery success, the other emphasizing warning signs. The project showcases my proficiency in large dataset analysis, statistical visualization design, and strategic data storytelling.

This project also serves as a critical warning that data representation can be biased. By demonstrating how identical datasets can be weaponized to support opposing conclusions, the work encourages viewers to approach all data presentations with healthy skepticism and critical thinking.