Presentation and workshop materials from Black in Data Week 2023, where I presented a comprehensive framework for structuring data science projects with reproducibility, transparency, and best practices in mind.
This repository contains a data science project template designed to guide analysts and researchers through the complete analytics workflow—from problem formulation through model evaluation and deployment considerations.
Black in Data Week 2023
November 2023
Virtual Conference
Many data science projects fail not due to lack of technical skills, but from poor project structure, unclear documentation, and lack of reproducible workflows. This presentation introduced a comprehensive project template addressing common pitfalls in data science work.
The template emphasizes:
- Clear problem formulation - Defining goals and success metrics upfront
- Transparent data processes - Documenting data sources, cleaning decisions, and transformations
- Reproducible analysis - Version control, dependency management, and code organization
- Rigorous evaluation - Moving beyond accuracy to understand model limitations
- Ethical considerations - Addressing bias, fairness, and interpretability
This framework is particularly valuable for health equity and social impact data science, where transparency and interpretability are critical.
This repository provides a structured approach to data science projects organized into seven key phases:
- Define the problem and its importance
- Identify stakeholders and end users
- Establish success criteria
- Articulate 2-3 specific, measurable objectives
- Distinguish between business goals and technical metrics
- Consider equity and fairness objectives
- Document data sources and provenance
- Describe data shape, size, and structure
- Detail cleaning processes and transformations
- Address missing data transparently
- Create reproducible data pipeline
- Document feature creation logic
- Explain feature selection rationale
- Consider fairness implications of feature choices
- Balance predictive power with interpretability
- Justify model selection
- Document hyperparameter tuning process
- Consider computational constraints
- Address model assumptions
Comprehensive evaluation beyond accuracy:
- Multiple performance metrics (precision, recall, F1, AUC)
- Confusion matrices and error analysis
- Cross-validation for generalization
- Bias-variance tradeoff analysis
- Assessment of overfitting/underfitting
- Feature importance and interpretability
- Fairness metrics (if working with demographic data)
- Uncertainty quantification
- Document obstacles and solutions
- Analyze tradeoffs in methodological decisions
- Propose improvements and extensions
- Consider deployment and maintenance
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Structure drives success - A clear project template prevents common pitfalls and improves collaboration
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Documentation is not optional - Future you (and your collaborators) will thank you for clear README files and commented code
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Model evaluation is more than accuracy - Understanding when and why models fail is critical, especially in high-stakes domains like healthcare
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Reproducibility builds trust - Version control, dependency management, and transparent processes are essential for credible data science
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Equity considerations should be upstream - Addressing fairness starts with problem formulation and data collection, not just model evaluation
- Data science workflow design
- Best practices in reproducible research
- Model evaluation and validation
- Science communication and teaching
- Community engagement in data science
- Equity-centered data science practices
When using this template for your own projects:
project-name/
├── README.md # Project overview and documentation
├── requirements.txt # Python dependencies
├── environment.yml # Conda environment (if using conda)
├── data/ # Data files (add to .gitignore if sensitive)
│ ├── raw/ # Original, immutable data
│ ├── processed/ # Cleaned, transformed data
│ └── README.md # Data documentation and sources
├── notebooks/ # Jupyter notebooks for exploration
│ ├── 01_data_exploration.ipynb
│ ├── 02_data_cleaning.ipynb
│ ├── 03_modeling.ipynb
│ └── 04_evaluation.ipynb
├── src/ # Source code for production
│ ├── __init__.py
│ ├── data_cleaning.py
│ ├── feature_engineering.py
│ ├── modeling.py
│ └── evaluation.py
├── outputs/ # Model outputs, figures, reports
│ ├── figures/
│ ├── models/
│ └── reports/
├── tests/ # Unit tests for code
└── .gitignore # Files to exclude from version control
This template is particularly valuable for health equity and disparities research, where:
- Transparency is critical - Stakeholders need to understand how conclusions were reached
- Bias detection is essential - Models must be evaluated for fairness across demographic groups
- Reproducibility builds trust - Findings may inform policy, requiring rigorous documentation
- Interpretability matters - Black-box models are often insufficient for healthcare applications
GitHub Repositories:
- Federal Health Equity Analytics Template - Demonstrates health equity analysis best practices
Writing:
- Health Innovation Newsletter - Monthly insights on healthcare AI and data science
Hobby, A. (2023). Data Science Project Template & Best Practices. Presented at Black in Data Week 2023.
For Reproducible Data Science:
- The Turing Way - Guide to reproducible research
- Good Enough Practices in Scientific Computing
- Cookiecutter Data Science
For Health Equity Data Science:
Andrea Hobby, DrPH Student
Johns Hopkins Bloomberg School of Public Health
Focus Areas: Algorithmic bias in healthcare AI, health equity analytics, patient safety, federal health equity reporting standards
Connect:
- GitHub: @AndreaHobby
- Newsletter: Health Innovation
- LinkedIn: Andrea Hobby
This presentation reflects my commitment to advancing diversity, equity, and excellence in data science and healthcare analytics. Special thanks to BlackTIDES for creating space for Black professionals to share knowledge, build connections, and elevate our collective impact.
Feel free to use this template for your own data science projects. When adapting it:
- Replace the template sections with your actual project content
- Document your decisions - Explain why you made specific choices
- Be transparent about limitations - Acknowledge what your analysis can and cannot tell you
- Make it reproducible - Include requirements.txt, clear instructions, and version information
- Consider equity - If working with demographic data, evaluate fairness metrics
Questions or suggestions for improving this template? Open an issue or reach out—I welcome feedback from the data science community.