Project Overview
Problem/Goal:
To apply data science techniques to NYC public school SAT performance data, developing analytical frameworks that identify performance patterns and support evidence-based educational policy and resource allocation decisions.
Data Source:
schools_data
The analytical dataset contains comprehensive SAT performance metrics across NYC public schools, including subject-specific scores and geographic classifications enabling data-driven educational insights.
Key Analytical Insights:
- Performance Excellence Identification: Data-driven identification of high-achieving educational institutions using quantitative performance metrics.
- Comprehensive Ranking Framework: Statistical ranking methodology evaluating overall academic performance across multiple assessment dimensions.
- Geographic Performance Distribution Analysis: Statistical analysis of performance variability across urban educational districts to inform resource allocation strategies.
Conclusion:
This data science framework demonstrates the application of analytical methodologies to educational assessment data, providing evidence-based insights that support strategic decision-making in public education policy, resource optimization, and academic program development.
Python Code and Outputs:
1. Which NYC schools have the best math results?
import pandas as pd
# Importing the data
schools = pd.read_csv("schools.csv")
# Top Schools for Math Performance
best_math_schools = schools[["school_name", "average_math"]]
best_math_schools = best_math_schools[best_math_schools["average_math"] >= 800 * 0.8].sort_values("average_math", ascending=False)
print(best_math_schools.head())
Output:
| School Name |
Average Math Score |
| Stuyvesant High School |
754 |
| Bronx High School of Science |
714 |
| Staten Island Technical High School |
711 |
| Queens High School for the Sciences at York College |
701 |
| High School for Mathematics, Science, and Engineering at City College |
683 |
2. What are the top 10 performing schools based on the combined SAT scores?
# Creating a column for total SAT scores
schools["total_SAT"] = schools["average_math"] + schools["average_writing"] + schools["average_reading"]
top_10_schools = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False).head(10)
print(top_10_schools)
Output:
| School Name |
Total SAT Score |
| Stuyvesant High School |
2144 |
| Bronx High School of Science |
2041 |
| Staten Island Technical High School |
2041 |
| High School of American Studies at Lehman College |
2013 |
| Townsend Harris High School |
1981 |
| Queens High School for the Sciences at York College |
1947 |
| Bard High School Early College |
1914 |
| Brooklyn Technical High School |
1896 |
| Eleanor Roosevelt High School |
1889 |
| High School for Mathematics, Science, and Engineering at City College |
1889 |
3. Which single borough has the largest standard deviation in the combined SAT score?
# Calculating the standard deviation for each borough
largest_std_dev = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2).rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"}).sort_values("std_SAT", ascending=False).head(1)
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)
Output:
| Borough |
Number of Schools |
Average SAT Score |
Standard Deviation of SAT Scores |
| Manhattan |
89 |
1340.13 |
230.29 |
