The Py City School Project provided me the opportunity to use Pandas DataFrames to analyze school and standardized test data. In this scenario, I served as the Chief Data Scientist for Py City's school district. I was tasked with assisting the school board and mayor in making strategic decisions about future school budgets and priorities.
Below is a snippet of my Jupyter Notebook. The full project is available on my GitHub.
To begin, I analyzed the district-wide standardized test results, including math and reading scores, as well as information about the schools. By aggregating and manipulating the data using Pandas, I was able to identify clear trends in school performance.
Throughout the project, I created a report that included various key metrics for the district and individual schools. I calculated the total number of unique schools, total students, total budget, average math and reading scores, and the percentages of students passing math, reading, and both subjects combined.
Additionally, I sorted the schools to identify the highest and lowest-performing schools based on the overall passing percentage. I also examined math and reading scores by grade level and analyzed school performance based on spending per student, school size, and school type.
Completing this project allowed me to demonstrate my skills in data analysis, manipulation, and visualization using Python and Pandas. The project was designed to provide insights into school performance trends. I provided actionable recommendations for budget allocation and strategic planning based on the analysis.
To view the complete project and explore the detailed findings, please visit my GitHub account. This project was completed as a part of UTSA's Data Analysis and Visualization Certification.
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