Data Analysis Project Starter Template

A fully structured, production-ready Data Analysis Project Starter designed for analysts, data scientists, and teams who want a scalable, organized, and reproducible foundation for data-driven projects.

This template enforces best practices in folder hierarchy, documentation, testing, and configuration — helping you focus on insights rather than setup.

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🚀 Getting Started

1. Use This Template (Recommended)

Click “Use this template” on GitHub to create a new repository from this starter. You’ll get a clean copy with no commit history.

2. Or Clone via Degit

If you prefer a lightweight copy without git history:

npx degit yourusername/data-analysis-project-starter my-project
cd my-project

(Replace yourusername with your GitHub handle and my-project with your project name.)

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🧭 Folder Structure Overview

data-analysis-project-starter/
│
├── config/
│   ├── config.yaml
│   ├── credentials_template.yaml
│   └── README.md
│
├── data/
│   ├── raw/
│   ├── processed/
│   ├── validated/
│   ├── final/
│   └── README.md
│
├── docs/
│   ├── SOP.md
│   ├── workflow.md
│   ├── project_overview.md
│   ├── findings_report.md
│   └── data_dictionary.md
│
├── notebooks/
│   ├── exploration.ipynb
│   ├── modeling.ipynb
│   └── experiments.ipynb
│
├── references/
│   ├── articles/
│   ├── datasets/
│   └── README.md
│
├── src/
│   ├── EDA/
│   │   ├── cleaning/
│   │   │   ├── clean_data.py
│   │   │   └── handle_missing_values.py
│   │   └── exploration/
│   │       ├── visualize_distributions.py
│   │       └── correlations.py
│   │
│   ├── quality_assurance/
│   │   ├── validate_data.py
│   │   └── detect_anomalies.py
│   │
│   ├── transformations/
│   │   ├── feature_engineering.py
│   │   ├── normalization.py
│   │   └── outlier_treatment.py
│   │
│   └── utils/
│       ├── helpers.py
│       ├── io_utils.py
│       └── logger.py
│
├── tests/
│   ├── test_data_validation.py
│   ├── test_transformations.py
│   └── test_utils.py
│
├── logs/
│   ├── qa_logs/
│   └── processing_logs/
│
├── .gitignore
├── LICENSE
├── requirements.txt
├── Makefile (optional)
└── README.md

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📂 Folder-by-Folder Breakdown

config/

Centralized project settings.

• config.yaml: Main configuration file (paths, parameters, etc.)
• credentials_template.yaml: Safe template for environment variables (no secrets)

data/

Your data pipeline layers.

• raw/: Unmodified source data
• processed/: Cleaned and intermediate datasets
• validated/: QA-checked data outputs
• final/: Ready-for-analysis or model-ready datasets

docs/

Project documentation and reporting.

• SOP.md: Standard Operating Procedure
• workflow.md: Workflow steps or data flow description
• findings_report.md: Final summary of insights or model results
• data_dictionary.md: Field-by-field variable descriptions

src/

Source code for analysis, processing, and utilities.

• EDA/: Exploratory Data Analysis scripts
• quality_assurance/: Validation and anomaly detection
• transformations/: Feature engineering, normalization, etc.
• utils/: Shared helper functions

notebooks/

Interactive analysis and modeling experiments.

tests/

All unit and integration tests for reproducibility and stability.

logs/

Log outputs for quality checks, ETL runs, or validation results.

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⚙️ Setup Instructions

1. Install Dependencies

pip install -r requirements.txt

2. Configure Project

Duplicate the credentials template:

cp config/credentials_template.yaml config/credentials.yaml

Edit paths and variables inside config/config.yaml as needed.

3. Run Validation or EDA Scripts

Example:

python src/EDA/cleaning/clean_data.py
python src/quality_assurance/validate_data.py

4. (Optional) Use Make Commands

If you added a Makefile, you can automate setup:

make setup
make validate
make tests

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🧪 Testing

Run all test scripts:

pytest tests/

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📘 Documentation Tips

For project teams:

• Keep docs/workflow.md up to date as the project evolves.
• Add versioned datasets in data/validated for traceability.
• Include visualization results or charts in docs/findings_report.md.

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🛡️ License

This template is distributed under the MIT License.

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🌟 Recommended Add-ons

• Dockerfile for environment consistency
• dataflow.md visual diagram (in docs/)
• pipelines/ folder for future orchestration (Prefect / Airflow)
• .env.example for environment variables

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🧠 Credits

Designed for data professionals who value:

• Clean structure
• Reproducibility
• Scalability
• Clarity in documentation

Crafted with care for real-world data teams, educators, and freelancers building impactful analytical workflows.