📅 2023-11-16 — Session: Analyzed and Optimized Academic Course Structures
🕒 20:30–23:50
🏷️ Labels: Python, Data Manipulation, Optimization, Education, Curriculum
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to analyze and optimize the structure of academic courses using data manipulation and optimization techniques in Python.
Key Activities:
- Data Filtering: Implemented a Python script to filter unrelated course pairs in the lower triangle of a DataFrame, enhancing data clarity.
- Optimization: Utilized cvxpy to solve a quadratic optimization problem, assigning course levels based on a matrix of correlativities.
- Data Replacement: Replaced values in a DataFrame that exceeded absolute 100 with NaN using Pandas’ applymap for better data handling.
- Difference Calculation: Developed a method using Pandas and NumPy to calculate semester differences between courses, facilitating data interpretation.
- Lower Triangle Calculation: Computed the lower triangle of differences between DataFrames and replaced zeros with NaN for improved visualization.
- Timeline Reconstruction: Reflected on a payment request event timeline to understand key actions and communications.
- Email Template Creation: Created a structured email template to communicate curriculum correlativity analysis findings.
- Academic Flexibility Analysis: Reflected on the flexibility of mathematics course sequences, proposing adjustments to course prerequisites.
Achievements:
- Successfully filtered and optimized course data, providing clear insights into academic course structures.
- Developed tools and templates to enhance communication and analysis of curriculum structures.
Pending Tasks:
- Further analysis of curriculum flexibility and potential adjustments to enhance academic offerings.