This repository focuses on utilizing Pandas DataFrames to analyze school and standardized test data.
The analysis comprises several key components:
- District-wide Summary Metrics: Includes total number of schools, total students, total budget, average math and reading scores, and percentages of students passing math, reading, and both subjects.
- Summary Metrics for Each Individual School: Provides details such as school name, type, total students, total budget, per student budget, average math and reading scores, and percentages of students passing math, reading, and both subjects.
- Identification of Top and Bottom Performing Schools: Based on overall passing percentage.
- Analysis of Student Performance by Grade Level: Examines math and reading subjects for each school.
- Examination of School Budget Allocation: Considers its correlation with student performance.
- Comparison of Student Performance Based on School Type: Compares Charter vs. District schools.
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School Performance by Type and Size:
- Top performing schools are predominantly Charter schools with smaller to medium-sized student populations.
- Bottom performing schools are primarily District schools with larger student populations, suggesting smaller class sizes and individualized attention may contribute to better academic outcomes.
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Budget Allocation and Student Performance:
- Interestingly, the budget per student in bottom performing schools is higher compared to top performing schools.
- This implies that simply increasing the budget may not directly improve student performance; instead, the quality of education delivery and resources utilization might play a more significant role.
This summary and analysis provide insights into school performance factors, aiding strategic decision-making regarding resource allocation and educational priorities.
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Data Analysis:
- Utilized Pandas and Jupyter Notebook to perform analysis on school and standardized test data.
- Calculated various metrics including average scores, passing percentages, and overall performance.
- Generated summary tables and DataFrames to present key findings.
- Analyzed trends in school performance based on different factors such as school type, size, and budget.
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Visualization:
- Presented the analysis results through tables and charts for better understanding.
- Used Matplotlib or other relevant libraries to create visual representations of the data where necessary.