My interest in data visualization began at FHI 360, where the work our data capture & visualization team showed me the power of translating monitoring, evaluation, and research data into interactive, user-friendly platforms. This led me to take a course in Tableau and learn PowerBI and Google Data Studio through project work at FHI 360. As a grad student at NYU, I built on this skillset by learning Python to build web-based, learning analytics applications. With a particular passion for research dissemination, I'm interested in continuing to explore how I can leverage data viz technologies to expand reach and engagement with research and learning.

I learned Python by taking a Building Apps for Learning Analytics course at NYU, ending the course by coding an app using PISA 2018 data. I integrated various Python libraries for different components of the app, including pandas for data cleaning, streamlit to build the user interface, and plotly to visualize the data.

My target audience included program designers interested in integrating ICTs into their education programming. The app enables program designers to explore how students vary across equity dimensions (e.g.: gender, language) on a range of PISA measures on ICTs, including availability, usage, autonomy, and competence.

A dashboard I led visual development on as part of a collaborative research study at FHI 360, entitled Education in Emergencies and Technology: Data Collection, Processing, and Use. Through a series of usability tests with EiE practitioners, we took the dashboard from low-fi prototypes on PowerBI to a final product on Google Data Studio. The final dashboard catalogues examples and insights of the use of data technologies in education in emergencies (EiE) to allow practitioners to i) compare across technologies, ii) examine insights by technologies, iii) identify tools for EiE, and iv) identify use cases of those tools in practice.