https://github.com/geobatpo07/ultralearning-ds-cloud
This repository offers a focused 3-month ultralearning plan to quickly develop practical skills in Data Science and Cloud Computing. Designed for professionals and career changers, it emphasizes hands-on projects, cloud deployment, and modern tools to help you stand out in a competitive job market.
https://github.com/geobatpo07/ultralearning-ds-cloud
cloud cloudcomputing datascience learning learning-by-doing machine-learning machine-learning-algorithms
Last synced: 3 months ago
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This repository offers a focused 3-month ultralearning plan to quickly develop practical skills in Data Science and Cloud Computing. Designed for professionals and career changers, it emphasizes hands-on projects, cloud deployment, and modern tools to help you stand out in a competitive job market.
- Host: GitHub
- URL: https://github.com/geobatpo07/ultralearning-ds-cloud
- Owner: Geobatpo07
- License: mit
- Created: 2025-07-18T05:39:22.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-18T07:21:17.000Z (3 months ago)
- Last Synced: 2025-07-18T11:18:07.953Z (3 months ago)
- Topics: cloud, cloudcomputing, datascience, learning, learning-by-doing, machine-learning, machine-learning-algorithms
- Homepage:
- Size: 187 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Roadmap: docs/roadmap.md
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README
# Ultralearning Data Science & Cloud
## Overview
This repository contains a structured 3-month ultralearning plan designed to rapidly build strong, practical skills in Data Science and Cloud Computing. It is tailored for professionals and career changers who want to quickly gain hands-on experience with the latest tools and workflows, making them highly competitive in today’s job market.
---
## 📅 Roadmap Summary
Month 1: Foundations
Build solid skills in Python programming, statistics, exploratory data analysis (EDA), data visualization, and cloud basics (AWS/GCP).
Month 2: Machine Learning & Cloud Architecture
Develop machine learning models, learn model evaluation, and deploy models as APIs using Docker and cloud services.
Month 3: MLOps & Final Projects
Implement MLOps workflows with tools like MLflow and Terraform, automate pipelines, and build end-to-end data science projects with cloud deployment and monitoring.
---
## 📂 Repository Structure
```
Ultralearning-DS-Cloud/
│
├── week_01/ # Weekly learning modules & projects
├── week_02/
├── ...
├── final_project/ # Comprehensive capstone project
├── docs/ # Roadmap, resources, and references
├── pyproject.toml # Poetry configuration file
├── poetry.lock # Poetry lock file
├── Dockerfile # Container setup for projects
└── README.md # This file
```
---
## 🚀 Getting Started with Poetry
1. **Install Poetry**: Follow the [Poetry installation guide](https://python-poetry.org/docs/#installation).
2. **Install dependencies**:
From the project root directory, run:
```
poetry install
```
3. **Activate the virtual environment**:
```
poetry shell
```
4. **Run your scripts or notebooks within the Poetry environment.**
---
## 📚 Resources & Tools
- Python libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
- Cloud platforms: AWS, Google Cloud Platform (GCP)
- Containerization & DevOps: Docker, GitHub Actions, Terraform
- MLOps frameworks: MLflow, DVC
- Learning platforms: Kaggle, Coursera, Fast.ai
---
## 🎯 Why This Plan?
- Rapid skill acquisition with focused, project-based learning
- Practical cloud deployment experience essential for modern data roles
- Strong foundation for career growth or transition in tech
---
## 📞 Contact
For questions or collaboration, feel free to reach out via GitHub Issues or [lgeobatpo98@gmail.com](mailto:lgeobatpo98@gmail.com).
---
Inspired by Scott Young’s Ultralearning methodology.
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.