Data Science Project Template

Group Name: [Your Group Name]
Authors: Author 1, Author 2, Author 3

The project template was initially developed by Ilia Azizi with main source here.

We use Quarto for the template, which starts from a single report.qmd file and can give several outputs:

• 🌐 HTML report called report.html
• 📑 PDF report called report.pdf
• 📝 Microsoft Word report called report.docx (you can also directly edit this instead of the report.qmd)

⚠️Please read the current file (README.md) very carefully!⚠️

Quick Start

Installation

1. Install Anaconda (if not already installed)

   • Download from anaconda.com
   • Follow installation instructions for your OS

2. Install Quarto

   • Download from quarto.org
   • Verify: quarto --version
   • Troubleshooting: Run quarto check to diagnose installation issues

3. Install TinyTeX (required for PDF output)

   quarto install tinytex

   • See Quarto PDF documentation for details

4. Set up project environment

   # Clone your repository
   git clone <your-repo-url>
   cd dsas_template
   
   # Create conda environment with required packages
   conda create -n dsas_project python=3.11
   conda activate dsas_project
   pip install -r requirements.txt

   Setting up conda with Quarto: See this guide for detailed instructions on configuring Quarto to work with your conda environment.

Render the Report

cd report
quarto render report.qmd

This generates HTML/PDF/DOCX files in the same directory.

Project Structure

.
├── data/
│   ├── raw/           # Original data (original data here, then do not modify!)
│   └── processed/     # Cleaned data
├── report/
│   ├── sections/      # Report sections (.qmd files)
│   ├── report.qmd     # Main report file
│   └── references.bib # Bibliography
├── src/               # Python modules
└── requirements.txt   # Package dependencies

Key rules:

• Never modify files in data/raw/ (ensures reproducibility)
• Use clear comments and docstrings in src/
• Each section in report/sections/ is a separate .qmd file

Working with Conda Environments

Conda manages Python environments to avoid package conflicts.

Basic commands:

# Activate your project environment
conda activate dsas_project

# Install additional packages
pip install package_name

# Deactivate when done
conda deactivate

# List all environments
conda env list

Best Practices

• Set random seeds for reproducibility: np.random.seed(42)
• Use version control (Git) and commit regularly
• Write clear commit messages explaining what changed
• Update .gitignore to exclude .venv/ or __pycache__/
• Document your code thoroughly
• Never commit credentials or large data files

Reproducibility Checklist

Your project is reproducible if anyone can:

1. Clone the repository
2. Create conda environment: conda create -n dsas_project python=3.11
3. Activate environment: conda activate dsas_project
4. Install dependencies: pip install -r requirements.txt
5. Render report: quarto render report/report.qmd

All results should match exactly.

Need Help?

• Ask during execise sessions.