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This repository focuses on utilizing Pandas DataFrames to analyze school and standardized test data.

Summary of Analysis

The analysis comprises several key components:

  1. 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.
  2. 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.
  3. Identification of Top and Bottom Performing Schools: Based on overall passing percentage.
  4. Analysis of Student Performance by Grade Level: Examines math and reading subjects for each school.
  5. Examination of School Budget Allocation: Considers its correlation with student performance.
  6. Comparison of Student Performance Based on School Type: Compares Charter vs. District schools.

Conclusions/Comparisons

  1. 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.

  2. 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.

Processes Done in the Assignment

  • 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.

  • 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.