Audit and Conversion of Data Science Artifacts

• Day: 2026-02-27
• Time: 19:55 to 20:50
• Project: Teaching
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Audit, Data Science, Jupyter, Methodology, Feature Engineering

Description

Session Goal:

The session aimed to audit and convert various data science artifacts, focusing on ensuring methodological robustness and efficient workflow management.

Key Activities:

1. Audit of Machine Learning Pipelines: Conducted a supervisor audit on a student’s machine learning pipeline to identify issues and ensure robustness in model evaluation.
2. Python Script Audit: Evaluated Python scripts for data ingestion and feature engineering, identifying schema inconsistencies and leakage risks.
3. Jupyter Notebook Conversion: Converted Jupyter notebooks to Python scripts using nbconvert, clearing outputs to streamline further analysis.
4. Methodological Analysis: Reviewed methodological issues in modeling and validation, addressing concerns about train-test split, cross-validation, and feature engineering.
5. Feature Selection Strategy: Discussed strategies for feature selection in ML models, emphasizing the importance of avoiding methodological leakage.

Achievements:

• Completed a detailed audit of data processing scripts and identified key areas for improvement.
• Successfully converted and managed Jupyter notebooks, enhancing workflow efficiency.
• Clarified methodological risks and proposed strategies for robust feature selection.

Pending Tasks:

• Further audit of data processing scripts with required file uploads.
• Implementation of recommended feature selection protocols to address identified risks.

Evidence

• source_file=2026-02-27.sessions.jsonl, line_number=0, event_count=0, session_id=1ac03a8955b77482bd1d0bb02d23c9d42284dfdd2d8e52b9968c4f33f78c45d2
• event_ids: []