This project was created during my course on big data’s ability to help the environment at the University of Florida. For this project, we were tasked with using Python, JupyterLab, and popular libraries like Pandas and Seaborn to wrangle and display data (retrieved from the National Ecological Observatory network) to support or disprove a hypothesis. My group chose to explore the possible correlation between the tree canopy height and plant species richness in specific areas.

Writing the code for this project gave me the opportunity to explore an unfamiliar area where programming is applied. I was able to refresh my skills in Python and learn the basics of new libraries. Thinking critically about how to properly wrangle large and complex datasets with code was also a very valuable lesson.