Name: "TODO: Name of the asset"
Category: "TODO: Model;Data pipeline;App"
Description: "TODO: Short description of what the asset is and how it's used."
Impact: "TODO: Short overview of the asset's business impact."
G6 lead: "TODO: Name of G6"
SRO: "TODO: Senior Responsible Owner for the project (usually SCS)."
Technical lead: "TODO: Data scientist responsible for the asset (usually G7 or G6)."
Business lead: "TODO: Lead from ops/policy/digital who commissioned or owns the product."
Last review date: "TODO: mmm-yy e.g. Jun-24"
Next review date: "TODO: mmm-yy e.g. Jun-24"
Outage Impact: "TODO: Red/Amber/Green"
Maintenance (FTE): "TODO: Minimum FTE required for ongoing support."
Documentation: "TODO: URL link to documentation."
Contact: "TODO: Contact email address for asset register communications."

Data Science Project Template

A comprehensive template repository for data science projects on the Analytical Platform, supporting both Python and R development with built-in code quality tools, testing frameworks, and CI/CD workflows.

Note

We are currently in development and suggestions are welcome! Open an issue here.

Quick Start

1. Click "Use this template" → Create repository
2. Clone: git clone https://github.com/moj-analytical-services/your-repo-name.git
3. Follow post-clone checklist below. More info in Setup Instructions.

Post-Clone Checklist

• Complete initial ethics scan (see Ethics & SAFE-D Framework)
• Create and activate virtual environment
• Install prek hooks: prek install
• Update this README with project details
• Register asset — complete the YAML block at the top of this README and add your repo to the Data Science Asset Register (see below)
• Update badge URLs in README
• Set GitHub repository description
• Grant team permissions (one Admin minimum)
• Review MoJ GitHub standards

Features

This template provides a robust foundation for data science projects:

• 🐍 Python, 🗄️ SQL & 📊 R Support: Pre-configured for all three languages with formatting and testing
• ✅ Code Quality Tools: prek hooks for automated formatting, linting, and security checks
• 🧪 Testing Framework: Three-tier test structure (unit, integration, end-to-end) with pytest and testthat
• 🔒 Security Scanning: Bandit for Python security, secrets detection, and container vulnerability scanning, as well as large file detection and nbstripout to detect if you're about to commit data
• 📝 Architecture Decision Records: Built-in ADR tooling for documenting important decisions
• 🐳 Docker Ready: Dockerfile included for containerised deployments on airflow using the Analytical platform workflow
• 🤖 CI/CD Workflows: GitHub Actions for automated testing, container builds, and releases to to the ECS for use with Airflow on the analytical platform.
• 📚 Comprehensive Documentation: README templates, ADR examples, and test documentation
• 🔄 PR and issue templates to match common data science ways of working

Project Structure

├── data/             # Data files (gitignored)
│   ├── raw/          # Original data
│   ├── processed/    # Cleaned data
│   └── external/     # Third-party data
├── docs/             # Documentation and ADRs
├── models/           # Trained models (gitignored)
├── notebooks/
├── references/       # Data dictionaries, manuals
├── reports/          # Generated outputs (gitignored)
│   └── figures/
├── scripts/          # Executable scripts
├── src/              # Reusable source code
│   ├── data/         # Data processing
│   ├── features/     # Feature engineering
│   ├── models/       # Training and prediction
│   └── visualization/# Plotting utilities
└── tests/
    ├── unit/
    ├── integration/
    └── e2e/          # End-to-end tests

See individual directories for detailed READMEs. Key points:

• data/, models/, reports/: Gitignored to prevent committing large or sensitive files
• src/: Installable as package with pip install -e . (see src/README.md)
• scripts/: One-off executables that use src/ code (see scripts/README.md)
• notebooks/: Use numbered prefixes with snake_case (e.g., 01_data_exploration.ipynb)

Tip

When starting a new project, create your module structure inside src/ (e.g., src/fraud_detection/) to keep code organised and importable.

Code Standards and Quality

This template uses prek hooks for automated code quality checks. The hooks cover:

• Python: Black formatting, Flake8 linting, Bandit security checks
• R: styler formatting, lintr linting (requires R packages: install.packages(c("styler", "lintr")))
• SQL: SQLFluff linting and formatting
• Notebooks: nbstripout to remove outputs
• General: trailing whitespace, file size limits, secrets detection

After setting up your environment, the hooks will run automatically on each commit. You can also run them manually:

prek run --all-files  # Run manually

Testing:

# Using uv (recommended - faster dependency installation)
uv venv && source .venv/bin/activate
uv pip install -r requirements-dev.txt
uv run pytest tests/              # Python tests (unit, integration, e2e)

# Or using standard pip
pytest tests/              # Python tests (unit, integration, e2e)

testthat::test_dir("tests/unit")  # R tests

Data Science Asset Register

All live data science products should be listed in the Data Science Asset Register. The asset register provides visibility of what is deployed, who owns it, and the support resource it requires.

Once you have filled in the YAML block at the top of this README, add your repository to the assets.yaml file in the asset register repo and open a pull request. Your asset will appear in the register the following morning.

Playbook

We use the MoJ Data Science and AI Playbook to guide projects through key stages.

Ethics & SAFE-D Framework

Important

MANDATORY REQUIREMENT: All data science and AI projects must complete the MoJ AI & Data Science Ethics Framework Process. This is not optional guidance; it is a required process for responsible development of data-driven technologies.

For detailed guidance, see CONTRIBUTING.md.

Setup Instructions

Once you've created your repository using this template, ensure the following steps:

Set Up Development Environment

We are aligned with the analytical platform's guidance on setting up a Python development environment.

1. Create a virtual environment (in your project directory):

   python3 -m venv venv

   Add venv to your .gitignore file (already included in this template).

2. Activate the virtual environment:

   source venv/bin/activate  # On macOS/Linux
   # or
   venv\Scripts\activate  # On Windows

   You'll see (venv) in your terminal prompt when activated.

3. Install development dependencies:

   pip install -r requirements-dev.txt

4. Install prek hooks (for code quality):

   prek install

5. Install project dependencies (when you have a requirements.txt):

   pip install -r requirements.txt

6. Record your dependencies:

   When you add new packages, update the requirements file:

   pip freeze > requirements.txt
   git add requirements.txt
   ...

Set Up R Development Environment (Optional)

If you're working with R:

1. Install R (if not already installed):

   Follow the R installation guide for your operating system.

2. Install required R packages for code quality:

   # In R console
   install.packages(c("styler", "lintr", "testthat"))

3. Install project-specific R packages:

   Create a file called install_packages.R in your project root and list your dependencies:

   # install_packages.R
   packages <- c(
     "dplyr",
     "ggplot2",
     "tidyr"
     # Add your packages here
   )
   
   install.packages(packages)

   Run it with: Rscript install_packages.R

4. Alternative - Use renv for dependency management:

   # Initialise renv for your project
   install.packages("renv")
   renv::init()
   
   # Install packages (they'll be tracked by renv)
   install.packages("dplyr")
   
   # Create a snapshot of your dependencies
   renv::snapshot()

Update README

Edit this README.md file to document your project accurately. Take the time to create a clear, engaging, and informative README.md file. Include information like what your project does, how to install and run it, how to contribute, and any other pertinent details.

Also make sure the badge urls are correct for your repository, e.g.:

[![prek](https://github.com/moj-analytical-services/YOUR-REPO-HERE/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/moj-analytical-services/YOUR-REPO-HERE/actions/workflows/pre-commit.yml)

Update repository description

After you've created your repository, GitHub provides a brief description field that appears on the top of your repository's main page. This is a summary that gives visitors quick insight into the project. Using this field to provide a succinct overview of your repository is highly recommended.

Grant Team Permissions

Assign permissions to the appropriate Ministry of Justice teams. Ensure at least one team is granted Admin permissions. Whenever possible, assign permissions to teams rather than individual users.

Read about the GitHub repository standards

Familiarise yourself with the GDS Way. These standards ensure consistency, maintainability, and best practices across all our repositories.

Manage Outside Collaborators

To add an Outside Collaborator to the repository, follow the guidelines for managing GitHub collaborators.

Update CODEOWNERS

(Optional) Modify the CODEOWNERS file to specify the teams or users authorised to approve pull requests.

Configure Dependabot

Adapt the dependabot.yml file to match your project's dependency manager and to enable automated pull requests for package updates.

Dependency Review

If your repository is private with no GitHub Advanced Security license, remove the .github/workflows/dependency-review.yml file.

Code of Conduct

This project follows the Ministry of Justice's Code of Conduct. Please be respectful and professional in all interactions.

License

This project is licensed under the MIT License - see the LICENSE file for details.

This license may not be appropriate for all projects, because it gives permission to anyone with access to the repo to open source a copy of it. Please review and change the license as necessary for your project.