Welcome to Data Mavericks – Group 13 of the MIT Emerging Talent Certificate Program. This repository is our shared workspace for tackling data-science challenges: gathering and cleaning datasets, experimenting with analytical methods, and turning results into clear insights.
Our final research direction focuses on how cryptocurrency can foster financial inclusion in crisis-affected countries. This domain emerged from our diverse team’s lived experience, with several of us witnessing firsthand how crypto enabled financial access during times of banking collapse or instability. This topic intersects data, finance, and humanitarian value.
- Data Mavericks
This repository serves as the hub for our group collaboration, where we:
- Collaborate on data-science projects: Explore real-world datasets together to sharpen our analytical and coding skills.
- Learn from each other: Share knowledge, resources, and tips to help everyone grow.
- Code & notebook reviews: Provide constructive feedback to improve quality, reproducibility, and best practices.
- Tell the story with data: Turn findings into clear visualizations and concise write-ups.
- Build community: Strengthen our team spirit as we progress through the MIT Emerging Talent Program.
- Focus Area: Our project explores the role of cryptocurrency in improving financial inclusion in countries affected by conflict or instability. We aim to analyze public datasets and case studies where crypto use has replaced or supplemented traditional banking systems.
We encourage every team member to jump in. You can contribute by:
- Picking up tasks – Check the Task Breakdown for your assignments or claim an open issue.
- Setting up locally – Follow Setup and Usage to install dependencies and run tests.
- Working in a feature branch – Name it
<firstname>/<short_description>(e.g.sukhrob/data_cleaning_pipeline). - Adding code or notebooks – Keep notebooks reproducible (clean outputs, random seeds fixed) and adhere to our lint rules.
- Submitting a pull request – Link any related issue, ensure CI passes, and confirm your work meets the Definition of Done.
- Reviewing peers’ PRs – Leave constructive comments, suggest improvements, and approve when ready.
- Opening issues or discussions – Use issue templates for bugs, ideas, or dataset proposals so we can track them transparently.
- Master professional GitHub workflows (branches, PRs, reviews, CI checks) for reproducible analytics.
- Build teamwork and collaboration skills by pairing on notebooks, code, and documentation.
- Explore the full data-science lifecycle—data collection, cleaning, modelling, and validation—on finance-related datasets.
- Apply advanced Python libraries (pandas, NumPy, scikit-learn, matplotlib/plotly, pytest) to create robust analysis pipelines, rich visualizations, and a comprehensive unit-test suite.
- Communicate insights clearly through well-designed visuals and concise, audience-appropriate write-ups.
| Name | GitHub | Time Zone |
|---|---|---|
| Ahmed Elhassan | Goutbi | GMT +4 |
| Anass Ziadah | ziadahanass | GMT +3 |
| Clement Mugisha | Bikaze | GMT +2 |
| Emre Biyik | emrebiyik | GMT +2 |
| Mustafa Mangal | Mustafa-Mangal | GMT +2 |
| Sukhrob Muborakshoev | suhrobmuboraksho | GMT -7 |
.
├── .github/
│ ├── ISSUE_TEMPLATE/
│ └── workflows/
├── .vscode/
├── 0_domain_study/
├── 1_datasets/
├── 2_data_preparation/
├── 3_data_exploration/
├── 4_data_analysis/
├── 5_communication_strategy/
├── 6_final_presentation/
├── collaboration/
├── notes/
├── .gitignore
├── .ls-lint.yml
├── .markdownlint.yml
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── guide.md
- Python 3.9+
- Git
- A code editor (VS Code, PyCharm, etc.)
git clone https://github.com/MIT-Emerging-Talent/ET6-CDSP-group-13-repo.git
cd ET6-CDSP-group-13-repopython -m venv .venv
source .venv/bin/activate # macOS/Linux
.venv\Scripts\Activate.ps1 # Windows PowerShellpip install -r requirements.txt
pre-commit installpytest -qdvc pulljupyter lab- Acquire and prepare data
- Analyze and model
- Create code, notebooks, visuals, and reports
- Review peers’ work
- Follow repo hygiene and update docs
Goal: Research relevant datasets that support our topic: The role of cryptocurrency in fostering financial inclusion in crisis-affected countries.
| Team Member | Task | Due Date |
|---|---|---|
| All members | Research and summarize 1–2 datasets | 18 Jun 2025 |
We will meet Tuesday after Evan’s workshop to review findings.
Save results to
0_domain_study/as short.mdfiles or notebooks.
A task is complete when:
- Code/notebooks pass tests and lint checks
- Documents are peer-reviewed and approved
- CI/CD runs without errors
- Git + GitHub
- Python 3.9+
- pandas, NumPy, scikit-learn
- matplotlib, Plotly
- Jupyter Lab
- pytest
- GitHub Actions
- Markdown docs
| Phase | Dates |
|---|---|
| Collaboration | May 27 – June 2 |
| Problem Identification | June 3 – June 16 |
| Data Collection | June 17 – June 30 |
| Data Analysis | July 1 – July 21 |
| Communicating Results | July 22 – Aug 11 |
| Final Presentation | Aug 12 – Aug 24 |
This project is licensed under the MIT License.