📅 2025-03-11 — Session: Developed SQL and Data Analysis Exercises
🕒 00:05–01:25
🏷️ Labels: SQL, Data Analysis, Education, Postgresql, Pandas
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to design and develop SQL queries and data analysis exercises to enhance educational resources and facilitate learning.
Key Activities
- Proposed a tabular structure for organizing programming exercises, allowing for easy search and reuse via SQL queries.
- Suggested AI integration for automatic classification and metadata generation.
- Discussed the creation of enriched datasets for programming exercises, defining key fields for educators and implementers.
- Presented standardized exercises for data manipulation and visualization using Pandas and Plotnine.
- Introduced Plotnine for creating declarative visualizations in Python, offering Seaborn and Matplotlib as alternatives.
- Detailed the process of data cleaning and exploratory data analysis using Pandas.
- Provided a guide for setting up PostgreSQL, creating databases, and executing SQL commands.
- Explored advanced PostgreSQL teaching approaches, focusing on lesser-known commands and terminal optimization.
- Investigated graphical interface tools for executing SQL in PostgreSQL, presenting options like pgAdmin, DBeaver, and DataGrip.
- Offered a quick guide for using DBeaver with PostgreSQL, including database connection and SQL execution.
Achievements
- Developed a comprehensive framework for SQL and data analysis exercises.
- Enhanced educational resources with enriched datasets and standardized exercises.
Pending Tasks
- Further exploration of AI integration for automatic metadata generation.
- Implementation of advanced PostgreSQL teaching strategies.