The data analysis in the folder PyCitySchool is made using the Pandas package in Python. The script, written using jupyter notebook can be found here.
The goal of this script is to analyze the district-wide standardized test results for a city's school district. Students math and reading score are accessible as well as information on the school they are attending. The ultimate goal of this analysis is highlighting trends to help making strategic decisions regarding school budgets and priorities.
The notebook contains the following tables:
- Summary of key metrics for the District.
- Summary of the key metrics for each School.
- Top five schools according to the overall percentage of students passing both reading and math tests.
- Bottom five schools according to the overall percentage of students passing both reading and math tests.
- Summary of the Math score per grade for each School.
- Summary of the Reading score per grade for each School.
- Break down of school performances based on average Spending Ranges per Student.
- Break down of school performances based on school size (total number of students).
- Break down of school performances based on school Type. The main conclusions that is possible to draw from this analysis are reported at the end of the notebook.
For sorting two functions shared by unutbu on stack overflow were used. Here is the original thread.
The data analysis in the folder HeroesOfPymoli is made using the Pandas package in Python. This exercise was solved for practicing more pandas methods. The script, written using jupyter notebook can be found here. The main conclusion that is possible to draw from this analysis are not reported since this exercise is not intended for grading.
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