Developed Educational Exercises for Data Science Course

• Day: 2025-03-10
• Time: 13:15 to 13:45
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Visualization, Matplotlib, Python, Education, Course Evaluation

Description

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:

1. 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.
2. 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.
3. 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]].
4. Course Evaluation: Conducted a detailed analysis of the Data Science course structure and content, identifying strengths and areas for improvement.
5. 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.

Evidence

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• event_ids: []