Data Source: PyCitySchools.ipynb file and rename it PyCitySchools_Challenge_testing.ipynb. Software: Python 3.9, Visual Studio Code 1.50.0, Anaconda 4.8.5, Jupyter Notebook 6.1.4, Pandas
Honor to have been chosen to analyze your data and have enjoyed working with Maria and the rest of your staff. Your company has been able to oversee our findings and give input on GitHub routinely. We cleaned and eliminated unnecessary information while maintaining the accuracy/integrity of your data. Initially we were informed of the following undertakings:
- A high-level snapshot of the district's key metrics, presented in a table format
- An overview of the key metrics for each school, presented in a table format
- Tables presenting each of the following metrics:
- Top 5 and bottom 5 performing schools, based on the overall passing rate
- The average math score received by students in each grade level at each school
- The average reading score received by students in each grade level at each school
- School performance based on the budget per student
- School performance based on the school size
- School performance based on the type of school
Maria notified my company with regards to the academic dishonesty occurring with math/reading scores at the ninth-grade level at Thomas High School. Your district has honorably wanted to uphold the academic standards set forth. In keeping with her request, we have analyzed the results nulling the scores from Thomas and accessing the rest of the data. After receiving the data replacements, we repeated our analysis and have summarized our findings as to the affect these changes have had overall.
After refactoring the code to replace the math/reading scores of the 9th grade THS students, the District Statistical Summary did not reflect a strong fluctuation. Changes ranging from 0 to 1.5% change. A measure of significance was not performed at this time but available if deemed necessary. A z-test can be carried out to find a z-score for your study; it would then be converted to a P-value. If your P-value is lower than the significance level, you can conclude that your observation is statistically significant.
Reviewing Thomas High School data, Percent Passing Reading and Percent Overall Passing appear to be the most significant percentage change. These statistics are worth further investigation.
The table below demonstrates the difference in scores comparing the rest of the schools in the district with that of TSH. The differences are incredibly significant in the Percent Passing Reading and Percentage Overall Passing. In Percent Passing Reading, the difference changes from a positive 8.7% in the original values to a negative 18.9. A change of 27.6 % for percentage passing reading. In Percent Overall Passing, the difference changes from a positive 19% in the original values to a negative 6.9. A change of 25.9 % for percentage passing reading.
The scores per grade were not change for the effects on the averages were only slightly altered.
Spending did not change but observing the data the more money spent the decreasing the students’ performance. Of course, this is possibly due to the increasing needs at the lower achieving schools. Also noted is the concern of the grade reported for math which are much less than those of reading. Allocation for such deviation might be a consideration.
School size scores remained the same through this altercation. An observation overall of performance verses size of school is incredibly significant, 32%. Ideas of breaking the larger schools into small dens might allow for more intimate atmosphere thus allowing for closer monitoring of students.
Type of school results remained consist with the initial summary. Significant to note here is the measurable difference between Charter and District. A study could be undertaken to see why this is the case and possible implementation of these successful characteristics in the district setting may prove beneficial.
After reading and math scores have been replaced, the comparison of the analysis before and after revealed four changes to the school district report. At the school level, only THS experienced significant changes in statistics. The average reading scores changed only slightly, 0.4% The differences are incredibly significant in the Percent Passing Reading and Percentage Overall Passing. In Percent Passing Reading scores, the difference changes from a positive 8.7% in the original values to a negative 18.9. A change of 27.6 % for percentage passing reading. In Percent Overall Passing, the difference changes from a positive 19% in the original values to a negative 6.9. A change of 25.9 % for percentage passing reading. Resulting in a 28.4 % and 28.5 decrease, from the initial statistics. The District Statistical Summary did not reflect a significant fluctuation. Changes ranging from 0 to 1.5%. The overall affect of this incident is isolated specifically to THS, with minor affects in the district data. Through the analysis, a few observations were revealed and knowing your dedication to excellence, we thought we would share them with you:
* The inverse relationship between money allocated per student verses students’ performance. Of course, this is possibly due to the increasing needs at the lower achieving schools. Also noted is the concern of the grade reported for math which are much less than those of reading. Allocation for such deviation might be a consideration.
* Significant to note here is the measurable difference between Charter and District. A study could be undertaken to see why this is the case and possible implementation of these successful characteristics in the district setting may prove beneficial.
Contact: jacquie0583@yahoo.com