This project aims to provide a better tool to analyze the Course and Professor Evaluations (CAPEs) data from the University of California, San Diego. This repository contains CSV files of CAPEs data and Python scripts for data cleaning, processing, and visualization.
The visualization is carried out in a Jupyter notebook and requires Python and several data processing and visualization libraries. Here are the requirements:
- Python 3.7.+
- Jupyter Notebook
- Pandas
- Numpy
- ipyaggrid
- ipywidgets
If you don't want to set up a local environment, you can run the notebook directly on Google Colab. Click the badge at the top of this README to open the notebook in Colab.
Please note that Google Colab environments are ephemeral, and you will lose all your changes once the session ends.
This is untested, but you can run the notebook locally by installing the required libraries. I recommend using a virtual environment to avoid conflicts with your Python installation system.
The analysis is based on the CAPEs data from the University of California, San Diego. The data includes course name, instructor, term, enrollments, evaluations made, average grades expected and received, weekly study hours, and recommendations for the course and instructor.
To make the data more user-friendly, if two or more rows share the same course name and instructor, the rows are combined into one row. The enrollments and evaluations made are summed. The average grades expected, received, recommendations, and weekly study hours are averaged. The term is set to the most recent term the instructor taught.
Feel free to open an issue or submit a pull request if you have any suggestions or ideas.