📅 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 session aimed to develop a comprehensive catalog of practical exercises for advanced Data Science courses, focusing on data visualization, exploratory analysis, and statistical concepts.
Key Activities
- Compiled a list of 20 practical exercises aligned with advanced Data Science classes, emphasizing data visualization and exploratory analysis.
- Proposed structuring exercises in a detailed format, potentially using interactive notebooks.
- Provided feedback on a catalog of exercises for teaching Python and Pandas, suggesting improvements for course organization.
- Created a collection of 15 rigorous exercises on probability, hypothesis testing, and regression for hard science students, including simulations and code examples.
- Detailed a practical exercise on A/B testing, focusing on Type I and II errors in an e-commerce context.
- Conducted an analysis of hypothesis testing for comparing proportions using a two-sample Z-test in Python.
- Suggested advanced statistical topics to enrich a statistics course, such as simulations and dimensionality reduction.
- Compiled a list of 20 exercises to help students master machine learning concepts through practical and theoretical approaches.
Achievements
- Successfully compiled and structured a diverse set of exercises covering key areas in Data Science and Statistics.
- Enhanced the educational content with practical examples and feedback for better engagement and understanding.