Create and manipulate Pandas DataFrames to analyze school and standardized test data.
The goal is to help the local school board and mayor make strategic decisions regarding future school budgets and priorities. This involves analyzing the district-wide standardized test results. The task will involve aggregating the data to showcase obvious trends in school performance.
District Summary
Calculations were performed to create a snapshot of the district's key metrics in a DataFrame. This will include the following:
* Total number of unique schools, students and budget
* Average math and reading scores
* Percentage of students who passed math, reading and who passed both math and reading
School Summary
Calculations were performed to create a DataFrame summarizing key metrics about each school. This will include the following:
* School name and type
* Total students and school budget
* Per student budget
* Average math and reading scores
* Percentage of students who passed math, reading and who passed both math and reading
DataFrames were then created to illustrate:
* Highest and lowest-performing schools
* Math and reading scores by grade
* Scores by school spending, school size and school type
A written report that presents a cohesive analysis which summarizes the analysis and draws two conclusions/comparisons from the calculations.
- Dataset provided by edX UofT Data Analytics, which had been generated by Trilogy Education Services, LLC.