🎓 NYC Academic Performance Audit: SAT Socio-Economic Trends

Project overview

Analyzing academic performance is vital for educational stakeholders to allocate resources effectively. This project performs a deep-dive audit of SAT results across New York City’s five boroughs, identifying not only top-performing institutions but also the geographic variance that indicates educational inequality.

🎯 The Mission

The goal of this project was to identify systemic trends in underperformance across public schools by analyzing a dataset of over 50,000 student records. Rather than just reporting numbers, I simulated an audit workflow to provide data-driven interventions for educational equity.

🛠️ Tech Stack & Methodology

Language: Python 3.10+ Libraries: Pandas (Vectorized operations), NumPy (Statistical computations), Matplotlib/Seaborn (Visualizing distribution) Key Techniques: Data Aggregation, Lambda Functions for Feature Engineering, and Standard Deviation Analysis to measure performance volatility.

📊 Objectives & Findings

1. High-Performance Math BenchmarkingMetric: Filtered for schools achieving $\ge 80%$ ($640+$ points) in Mathematics.Insight: Identified a elite cluster of schools, providing a benchmark for curriculum success that can be modeled in other districts.
2. Composite Score EngineeringAction: Engineered a new feature, total_SAT, by aggregating Math, Reading, and Writing scores to provide a holistic view of student success beyond specialized subjects.Top 10 Analysis: Ranked the city's highest-achieving schools to identify geographic "Education Hubs."
3. Borough-Level Variance Audit (The "Job-Ready" Insight)Method: Grouped data by borough to compute the Mean and Standard Deviation ($\sigma$).Critical Finding: Manhattan exhibited the highest variability in scores ($Std \ Dev \approx 230.29$).

Business Impact : High variance suggests a wide "achievement gap" within the same borough, highlighting where targeted government interventions are most needed compared to boroughs with more uniform performance.

📂 Dataset

• File: schools.csv

• Columns used:

  • school_name
  • borough
  • average_math
  • average_reading
  • average_writing

https://data.cityofnewyork.us/Education/2012-SAT-Results/f9bf-2cp4/about_data

🗂️ Repository Structure

nyc-sat-analysis/ ├── data/ │ └── schools.csv ├── notebooks/ │ └── analysis.ipynb ├── reports/ │ └── figures/
├── src/ │ └── analysis.py ├── README.md ├── requirements.txt └── .gitignore

requirements.txt pandas>=2.0 numpy jupyter

👩‍💻 Author

Olayinka Agbaje| Data Scientist

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Run the notebook

jupyter notebook notebooks/analysis.ipynb

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## ✅ Results


* **Top Math Schools:** e.g., `[School A, School B, …]`
* **Top 10 Total SAT Schools:** `[Manhattan]`
* **Borough with highest SAT variability:** `Manhattan (Avg SAT = 1340.13, Std Dev = 230.29)