Analyzing school and standardized test data to showcase obvious trends in school performance.
Pandas and Jupyter Notebook
- district summary: after analyzng the district-wide standardized test results, I aggregated the data to create a high-level snapshot of the district's key metrics in a DataFrame. It includes total number of schools and students, total budget, average and passing math and reading scores as well as overall passing scores across the schools.
- school summary: after performing necessary calculations, I created a DataFrame that summarizes key metrics about each school.It includes school type, school size (total number of students), school budget both total and per student, average and passing math and reading scores.
- As a whole, reading scores are a little bit higher than math scores,across different types of schools.
- As a whole, schools with higher budgets, did not yield better test results. By contrast, schools with higher spending 645-675 per student actually underperformed compared to schools with smaller budgets (585 per student).
- As a whole, smaller and medium sized schools dramatically out-performed large sized schools on passing math performances (89-91% passing vs 67%).
- As a whole, charter schools out-performed the public district schools across all metrics. However, more analysis will be required to glean if the effect is due to school practices or the fact that charter schools tend to serve smaller student populations per school.