Selected projects in data analysis and information visualization using an interdisciplinary, human-centered, and accessible approach.
My work focuses on bringing an interdisciplinary and human-centered perspective to data analysis and information visualization with an emphasis on critical thinking, clear communication, and accessible design principles. I recently completed a Master of Information in Data Science from Rutgers University School of Communication & Information, where I developed projects using tools including Python, SQL, Tableau, and web technologies to explore real-world datasets.
My goal is to leverage cutting-edge analysis techniques to solve complex problems and make a positive impact on local communities. I also bring a distinctly global perspective to my approach. Prior to transitioning into data science, I spent nearly a decade living and working in dynamic roles within the education sector across four countries on three different continents. These experiences strengthened my interest in clearly communicating complex information for an inclusive audience and designing accessible digital resources.
This project examines electric vehicle adoption trends, public charging infrastructure accessibility, and renewable energy production across Ireland using national datasets. The analysis integrates data from the Central Statistics Office (CSO), Sustainable Energy Authority of Ireland (SEAI), and ESB Networks to explore regional patterns in EV adoption and charging infrastructure and identify areas of inequity or opportunity for improvement. The resulting report investigates relationships between infrastructure availability, population distribution, electric vehicle adoption, and renewable energy production on a county-level basis.
Methods: Exploratory analysis, comparative analysis, geospatial analysis (including nearest-neighbor), and statistical correlation and linear regression.
Tools: Python, Pandas, GIS