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Organizing data into immutable raw, interim, and processed stages to track lineage.
Distinct from Data Categorization: Focuses on the lifecycle stages of data processing rather than classifying data by content or metadata.
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This project provides a standardized project directory structure and boilerplate templates for organizing data analysis and machine learning workflows. It serves as a reproducible analysis framework and workspace boilerplate designed to ensure consistency across data science projects. The template distinguishes between exploratory research in notebooks and reusable, testable logic in modular Python packages. It enforces a convention-based directory hierarchy that treats the analysis pipeline as a directed acyclic graph by separating raw, interim, and processed data. The framework covers a br
Separates raw, interim, and processed data into distinct folders to maintain a clear lineage from source to final set.