📅 2025-03-10 — Session: Developed Educational Exercises for Data Science Course
🕒 13:15–13:45
🏷️ Labels: Data Visualization, Matplotlib, Python, Education, Course Evaluation
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to develop and refine educational exercises for a Data Science course, focusing on data visualization, exploratory data analysis, and storytelling with data.
Key Activities:
- Creation of Scientific Graphs with Matplotlib: Developed exercises for students to create high-quality scientific graphs using Matplotlib and Zipf’s law, focusing on log-log scale visualization.
- Exploratory Data Analysis Report: Guided students in selecting open datasets, documenting them, and performing exploratory data analysis (EDA) using Python and Markdown in Jupyter Notebook.
- Data Storytelling Exercise: Designed a workshop for students to create narratives with data, using relevant topics like climate change and COVID-19, emphasizing the importance of storytelling in data visualization.
- Course Evaluation: Conducted a detailed analysis of the Data Science course structure and content, identifying strengths and areas for improvement.
- Probability and Statistics Module Evaluation: Reviewed the Probability and Statistics module for Machine Learning, providing insights and suggestions for enhancement.
Achievements:
- Developed comprehensive exercises for scientific graph creation and data storytelling.
- Completed an exploratory data analysis exercise template.
- Provided detailed evaluations of course modules, offering constructive feedback for future improvements.
Pending Tasks:
- Implement suggested improvements in the Probability and Statistics module.
- Further refine the data storytelling workshop with additional real-world examples.