Data Mavericks

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 specific research angle is still being finalized, but we expect to dive into finance-focused datasets and problems.

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Table of Contents

1. Project Overview
2. How to Contribute
3. Project Goals
4. Team Composition
5. Repository Structure
6. Setup and Usage
7. Roles and Responsibilities
8. Task Breakdown
9. Definition of Done
10. Tools and Technologies
11. Milestones
12. License

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Project Overview

This repository serves as the hub for our group collaboration, where we:

1. Collaborate on data-science projects: Explore real-world datasets together to sharpen our analytical and coding skills.
2. Learn from each other: Share knowledge, resources, and tips to help everyone grow.
3. Code & notebook reviews: Provide constructive feedback to improve quality, reproducibility, and best practices.
4. Tell the story with data: Turn findings into clear visualizations and concise write-ups.
5. Build community: Strengthen our team spirit as we progress through the MIT Emerging Talent Program.

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How to Contribute

We encourage every team member to jump in. You can contribute by:

1. Picking up tasks – Check the Task Breakdown for your assignments or claim an open issue.
2. Setting up locally – Follow Setup and Usage to install dependencies and run tests.
3. Working in a feature branch – Name it <firstname>/<short_description_snake_case> (e.g. sukhrob/data_cleaning_pipeline).
4. Adding code or notebooks – Keep notebooks reproducible (clean outputs, random seeds fixed) and adhere to our lint rules.
5. Submitting a pull request – Link any related issue, ensure CI passes, and confirm your work meets the Definition of Done.
6. Reviewing peers’ PRs – Leave constructive comments, suggest improvements, and approve when ready.
7. Opening issues or discussions – Use issue templates for bugs, ideas, or dataset proposals so we can track them transparently.

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Project Goals

1. Master professional GitHub workflows (branches, PRs, reviews, CI checks) for reproducible analytics.
2. Build teamwork and collaboration skills by pairing on notebooks, code, and documentation.
3. Explore the full data-science lifecycle—data collection, cleaning, modelling, and validation—on finance-related datasets.
4. Apply advanced Python libraries (pandas, NumPy, scikit-learn, matplotlib/plotly, pytest) to create robust analysis pipelines, rich visualizations, and a comprehensive unit-test suite.
5. Communicate insights clearly through well-designed visuals and concise, audience-appropriate write-ups.

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Team Composition

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

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Repository Structure

Click to expand/collapse the repository structure

.
│   .gitignore
│   .ls-lint.yml
│   .markdownlint.yml
│   CONTRIBUTING.md
│   LICENSE
│   README.md
│   guide.md
│
├───.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 

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Setup and Usage

Prerequisites

• Python 3.9 +
• Git
• A code editor (VS Code, PyCharm, etc.)

1 · Clone the repository

git clone https://github.com/MIT-Emerging-Talent/ET6-CDSP-group-13-repo.git
cd ET6-CDSP-group-13-repo

2 · Create and activate a virtual environment

python -m venv .venv
# macOS / Linux
source .venv/bin/activate
# Windows (PowerShell)
.venv\Scripts\Activate.ps1

3 · Install project dependencies

pip install -r requirements.txt          # add this file when you freeze libs
pre-commit install                       # optional: auto-lint on every commit

4 · Run the test suite

pytest -q

5 · (If needed) download large datasets

# Example: pull data versioned with DVC or Git LFS
dvc pull                           # or: git lfs pull

6 · Launch Jupyter Lab for notebooks

jupyter lab

Tip: keep notebooks in a dedicated notebooks/ folder to minimise merge conflicts.

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Roles and Responsibilities

Core Responsibilities (everyone)

• Acquire and prepare data – source datasets, clean them, and document provenance.
• Analyze and model – explore patterns, build statistical / ML models, and validate results.
• Create artifacts – code modules, notebooks, visualizations, and concise reports.
• Review and improve – give constructive feedback on peers’ pull requests and notebooks.
• Maintain project hygiene – follow branch naming, pass CI checks, and update documentation.

Team Members

• Ahmed Elhassan
• Anass Ziadah
• Clement Mugisha
• Emre Biyik
• Mustafa Mangal
• Sukhrob Muborakshoev

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Task Breakdown

Week of 3 – 16 June 2025 · Problem Identification

Goal: Each team member drafts one finance-data-science project idea to discuss in this week’s meeting.
Include ⬇️

1. Problem statement (1–2 sentences)
2. Potential dataset(s) / data source
3. Expected value or insight
4. Sketch of analysis approach (very high-level)

Team Member	Deliverable	Due (before meeting)
Ahmed Elhassan	Project-idea brief (≤1 page)	14 Jun 2025
Anass Ziadah	Project-idea brief (≤1 page)	14 Jun 2025
Clement Mugisha	Project-idea brief (≤1 page)	14 Jun 2025
Emre Biyik	Project-idea brief (≤1 page)	14 Jun 2025
Mustafa Mangal	Project-idea brief (≤1 page)	14 Jun 2025
Sukhrob Muborakshoev	Project-idea brief (≤1 page)	14 Jun 2025

Tip: Save your idea as 0_domain_study/ideas/<firstname>/idea.md (or a short notebook) in a feature branch named firstname/project_idea.

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Definition of Done

A task is complete when:

1. Code modules and notebooks pass automated tests, lint checks, and receive approval in code review.
2. Collaboration documents are reviewed and approved.
3. CI/CD pipelines run successfully.

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Tools and Technologies

• Version Control: Git + GitHub
• Project Management: GitHub Issues & Project boards
• Languages / Libraries
  • Python 3.9+
  • pandas · NumPy · scikit-learn
  • matplotlib / Plotly (visualization)
• Environment: Jupyter Lab · virtualenv
• Testing: pytest
• CI/CD: GitHub Actions (lint, unit tests)
• Documentation: Markdown (MkDocs optional for future docs site)

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Milestones

Phase	Date Range (2025)
Cross-Cultural 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 – August 11
Final Presentation	August 12 – August 24

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License

This project is licensed under the MIT License.