📅 2025-03-10 — Session: Developed Data Science Exercise Catalog
🕒 04:30–06:00
🏷️ Labels: Data Science, Education, Exercises, Visualization, Statistics, Machine Learning
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop a comprehensive catalog of practical exercises for advanced Data Science courses, focusing on data visualization, exploratory analysis, and statistical concepts.
Key Activities
- Created a list of 20 practical exercises aligned with advanced Data Science classes, emphasizing data visualization and exploratory analysis.
- Compiled a collection of exercises with suggested open datasets for practical application.
- Proposed structuring exercises in detailed formats, including instructions and expected solutions or interactive notebooks.
- Provided feedback on a catalog of exercises for teaching Python and Pandas, highlighting areas for improvement.
- Developed a collection of rigorous exercises on probability, hypothesis testing, and regression for hard science students, including simulations and code examples.
- Detailed practical exercises on A/B testing, focusing on Type I and II errors and proportion hypothesis testing.
- Suggested advanced statistical topics to enrich a statistics course, including simulations and model validation.
- Outlined 20 strategic exercises for mastering machine learning concepts through practical implementation.
Achievements
- A comprehensive catalog of exercises was developed for various aspects of Data Science and Statistics education.
Pending Tasks
- Finalize the detailed structure and format for each exercise, including interactive notebooks where applicable.
- Incorporate feedback to enhance course organization and engagement.