There are 2 data sets provided - school data and student data. The key elements provided in the school data are 'School Name', 'School Type' and 'Budget' of all the schools in a school district. The key elements provided in the student data are 'School Name', 'Grade', 'Reading Score' and 'Math Score' of each student.
Following analysis were performed after joining above 2 datasets on 'School Name' field
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District Summary - The overall stats of schools in the district is calculated.
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School Summary - The data is summarized here for each school.
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Highest Performing School Summary - Top 5 schools that had highest 'Overall Passing' percentage is shown here.
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Bottom Performing School Summary - 5 schools that had lowest 'Overall Passing' percentage is shown here.
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Math Scores By Grade - The average math score of 9th, 10th, 11th & 12th graders of each school is shown here.
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Reading Scores By Grade - The average reading score of 9th, 10th, 11th & 12th graders of each school is shown here.
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Scores by School Spending - The 'Spending Ranges (Per Student)' for each school is shown here.
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Scores by School Size - A new bucket to categorize the schools based on total number of students is added here. Here are the buckets used for this categorization - "Small (<1000)", "Medium (1000-2000)", "Large (2000-5000)"
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Scores by School Type - Here the overall stats are looked at by the 2 school types - Charter and District.
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The overall passing percentage was higher for schools that spend least amount of money per student.
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The charter schools had higher overall passing percentage compared to the district schools.
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Large schools with 2000-5000 students had lowest overall passing percentage.
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Cabrera High School had the highest overall passing percentage (91.33%).
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Rodriguez High School had the lowest overall passing percentage (52.99%)
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The percentage of students who passed reading is higher compared to Math.
- Source Code: PyCitySchools/PyCitySchools_starter.ipynb
- Dataset: PyCitySchools/Resources/students_complete.csv PyCitySchools/Resources/schools_complete.csv
- Open a terminal
- Confirm condo version
conda --version\ - Confirm jupyter version
jupyter --version\ - Activate conda environment
conda activate dev\ - Launch Jupyter Notebook
jupyter notebook\ - Jupyter Notebook is opened in a browser
- Open "PyCitySchools/PyCitySchools_starter.ipynb" file using Jupyter Notebook
- Click on 'Cell > Run All' to run
This repo was published for educational purpose only. Copyright 2023 edX Boot Camps LLC. All rights reserved.