Jupyter Notebook Guide

A skill for using Jupyter notebooks effectively in research contexts. Covers notebook organization, reproducibility best practices, collaboration workflows, and integration with research computing infrastructure.

Notebook Organization

Recommended Structure

Every research notebook should follow a consistent structure:

01_data_collection.ipynb     # Data acquisition and initial storage
02_data_cleaning.ipynb       # Preprocessing, validation, transformations
03_exploratory_analysis.ipynb # EDA, descriptive statistics, initial plots
04_modeling.ipynb             # Model training, evaluation, selection
05_results_visualization.ipynb # Publication-quality figures
06_supplementary.ipynb       # Additional analyses, robustness checks