This repository encapsulates an in-depth analysis of a decade's worth of US technical and vocational education funding data, sourced from the National Center for Education Statistics' Local Education Agency (LEA) Finance Survey. It demonstrates a sophisticated data analysis workflow, from crafting a PostgreSQL database to creating nuanced visualizations that render complex data into intelligible insights.
src/: Jupyter Notebooks detailing the analytical pipeline with Python scripts for data processing and visualization.PDF Documentation/: In-depth dataset documentation from NCES and an Entity-Relationship Diagram (ERD) outlining the database architecture.SQL Queries/: SQL scripts crafted for precise data extraction and rigorous analysis.Local Education Agency Finance Survey – School District Data ERD.pdf: Detailed ERD visualizing the relational database design.
- A meticulously designed PostgreSQL database ensuring efficient data handling.
- Comprehensive Python-based data cleansing, ETL processes, and normalization techniques.
- Interactive, Plotly-based visualizations that highlight trends and funding trajectories in an engaging manner.
- Statistical analysis techniques applied to draw out patterns, growth rates, and significant fluctuations in funding streams.
Experience the project's data visualizations interactively through Binder-hosted Jupyter Notebooks. Click the corresponding Binder badges to launch a virtual instance where you can run and interact with the notebooks directly from your browser, no local setup required.
National Level Analysis:
Regional Level Analysis:
Please Note: Binder may take a few moments to prepare the environment. I appreciate your patience. Upon loading, execute the notebook by selecting "Restart Kernel and Run All Cells" to see the analysis in action.
Interactive Tableau Dashboard:
Explore the disparities in tech and vocational educational expenses from 2010 to 2020 through this Tableau Public Dashboard.
To delve into this analysis:
- Clone the repository to your local machine.
- Install the necessary libraries as specified in
requirements.txt. - Navigate through the Jupyter Notebooks for a step-by-step breakdown of the analyses.
- Refer to the PDF documentation for a deeper understanding of the dataset nuances.
- Review the ERD to comprehend the database schema.
A noteworthy finding is the substantial 114.38% increase in the cost per student between 2014 and 2016, largely attributable to the introduction of new expenditure categories. The analysis also discerns a significant growth in teacher salaries for vocational education, marked by a 7.35% upswing in 2020.
- Data: Sourced from the National Center for Education Statistics (NCES).
- Tools: Utilized Python, Jupyter, Plotly, PostgreSQL, among other open-source software.