📅 2025-06-18 — Session: Developed and Evaluated Academic Data Exercises
🕒 05:30–07:30
🏷️ Labels: Data Modeling, SQL, Machine Learning, Data Quality, Education
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and evaluate various academic exercises and exams focusing on data modeling, SQL pipelines, machine learning evaluation, and data quality.
Key Activities
- Proposed a modeling exercise for a banking system, including an entity-relationship diagram (ERD) and normalization steps.
- Detailed steps for creating a banking model, mapping to a relational model, and critically evaluating the design.
- Discussed true/false questions for machine learning models focusing on F1 score and hyperparameter evaluation.
- Proposed a regression exam using decision trees, addressing overfitting and underfitting.
- Analyzed an ER diagram for academic management systems, including normalization and critical evaluation.
- Developed SQL exercises for teaching data pipelines and aggregation patterns.
- Structured exams for topics A, B, and C, including comparative evaluations and rubric creation.
- Outlined data quality exercises using pandas for educational purposes, emphasizing analytical thinking.
- Designed a four-month programming and data analysis course, combining in-person and virtual classes.
Achievements
- Successfully structured and evaluated various academic exercises and exams.
- Developed a comprehensive curriculum for a data analysis course.
- Enhanced understanding of data modeling, SQL, machine learning, and data quality concepts.
Pending Tasks
- Finalize the course structure and resources, including a canonical document and editable table for coordination.
- Continue refining exam questions and evaluation rubrics.