This repository provides a comprehensive, prioritized roadmap for individuals aspiring to enter or advance within the data science and machine learning fields. It aims to be a singular reference point, guiding users through essential skills, resources, and portfolio projects to mitigate the overwhelming nature of self-directed learning in data science.

How It Works

The roadmap is structured around three primary career paths: Data Analyst, General Data Scientist, and Machine Learning Engineer. It offers a detailed breakdown of core skills, categorized by role, with specific tools and concepts to learn. The approach emphasizes a foundational understanding, progressing to specialized skills, and includes curated lists of recommended books, libraries, and community resources.

Quick Start & Requirements

• Installation: No direct installation is required; this is a curated resource guide.
• Prerequisites: Access to the internet is necessary. Specific tools mentioned (Excel, Tableau, R, Python, Docker, AWS/GCP/Azure) will require individual installation and setup.
• Resources: Links to external courses, certifications, and tools are provided throughout the README.

Highlighted Details

• Detailed skill breakdown for Data Analyst, General Data Scientist, and Machine Learning Engineer roles.
• Curated lists of essential books, libraries, and community resources.
• Guidance on building a portfolio with specific project examples and hosting recommendations (GitHub Pages, Kaggle).
• Recommended certifications and alternative learning paths (Kaggle, LinkedIn).

Maintenance & Community

• The repository is associated with a course, "CORE: Data Science and Machine Learning," suggesting a structured learning path.
• Links to community resources like Kaggle and LinkedIn are provided.

Licensing & Compatibility

• The repository itself does not appear to have a specific license mentioned in the README. The content is a guide to learning various tools and concepts, which are governed by their respective licenses.

Limitations & Caveats

• While comprehensive, the roadmap relies on external resources, some of which may have associated costs (e.g., certifications).
• The effectiveness of the roadmap is dependent on the user's self-discipline and ability to acquire and practice the listed skills.