In this project, I developed a comprehensive analysis of school performance data using Pandas DataFrames to help guide strategic decisions for the local school district. Acting as the Chief Data Scientist, I focused on evaluating district-wide metrics related to student performance on standardized tests in math and reading.
District-Level Analysis: Provided a high-level overview of district metrics, including the number of schools, total budget, and average student performance. Calculated metrics such as average math and reading scores, and percentages of students meeting passing thresholds for each subject and overall.
School-Level Analysis: Created a detailed performance report for each school, covering school type, budget allocations, per-student spending, and student achievement levels. Key insights included average math and reading scores and the percentage of students passing each subject per school.
Performance Ranking: Sorted schools based on their overall passing rates to identify the top and bottom performers, revealing trends in resource allocation and student outcomes across the district.
Grade-Level Performance Trends: Broke down math and reading scores by grade (9th–12th) across schools, enabling the school board to pinpoint areas for targeted academic support by year level.
Insights by Spending and School Size: Analyzed school performance in relation to spending levels and school size. Using binned data on per-student spending and school sizes, I generated reports showing average scores and passing rates across spending ranges and school sizes, providing actionable insights for budget allocation.
School Type Analysis: Examined the performance variations by school type (e.g., public vs. charter schools) to provide a data-driven basis for future funding and policy decisions.
This analysis highlighted key factors influencing student success, such as the impact of per-student spending and school size on performance. Two significant trends included a positive correlation between higher spending per student and passing rates, and a clear performance disparity between school types. These findings, visualized through detailed summaries, graphs, and tables, were used to support strategic recommendations for improving student outcomes and optimizing resource distribution across the district.