drivendataorg/cookiecutter-data-science

Features

• Project Scaffolding - Generates a standardized directory hierarchy and boilerplate files to bootstrap consistent data science projects.
• Machine Learning Scaffolds - Provides a standardized boilerplate for organizing machine learning project directories, data stages, and model artifacts.
• Data Stage Categorization - Separates raw, interim, and processed data into distinct folders to maintain a clear lineage from source to final set.
• Filesystem Hierarchies - Provides a standardized directory structure to ensure data lineage and a reproducible analysis pipeline.
• Data Science Project Templates - Provides a standardized project directory structure for organizing data analysis and machine learning workflows.
• Workspace Generators - Generates a complete project workspace layout from templates, including makefiles and modular package structures.
• Interactive Data Science Environments - Bootstraps a professional workspace with integrated support for linting, testing, and environment management.
• Analysis Workspace Organization - Provides a structured layout for organizing raw data files, extraction scripts, and database credentials.
• Immutable Data Patterns - Treats the analysis pipeline as a directed acyclic graph by utilizing immutable data flow patterns.
• Modular Package Systems - Organizes analysis logic into a formal Python package to facilitate code reuse across notebooks and scripts.
• Project Bootstrapping Tools - Provides CLI-driven bootstrapping to generate a standardized and reproducible data science workspace.
• Project Organization - Establishes a predefined directory hierarchy to separate raw data, processed sets, and model artifacts.
• Project Structures - Enforces a consistent directory hierarchy to maintain a clear lineage from raw data to final analysis outputs.
• Analytical Reproducibility - Ensures analytical reproducibility by isolating raw data from processed sets and exploratory work from production code.
• Exploration and Production Separation - Enforces a strict separation between exploratory notebooks and testable, portable source code.
• ML Asset Management - Organizes a project hierarchy to track data lineage and store serialized models and intermediate artifacts.
• Model Management - Creates dedicated storage locations for serialized models, predictions, and model summaries.
• CLI Task Runners - Implements a CLI-based task runner for orchestrating environment setup and data management.
• Environment Bootstrapping - Provides a foundation for replicating the computational environment through package manager and VM integration.
• Environment Configuration Management - Generates configuration files for environment managers to handle dependency locking and package installation.
• Environment Variable Loaders - Includes mechanisms to load sensitive credentials from external environment files to keep secrets out of version control.
• Exploration to Production Bridges - Provides a structural bridge between rapid iteration in notebooks and reusable, production-ready source code.
• Task Automation Tools - Supplies a centralized makefile to automate repetitive development tasks and environment synchronization.
• Source Code Modularization - Encourages wrapping analysis logic into formal Python packages to allow shared code to be imported into notebooks.
• Research and Analysis Workflows - Segments project directories for notebooks, reports, and figures to organize the end-to-end research analysis workflow.
• Secrets and Credential Management - Isolates sensitive credentials from version control using environment files and automated loader mechanisms.
• Data Engineering Workflow Patterns - Separates data ingestion, feature engineering, and model training into modular Python components.
• Data Science Environments - Standardizes project structures for data science.

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