This repository curates a comprehensive list of educational resources for aspiring and practicing data scientists, covering the entire data science lifecycle from business problem definition to model deployment and ethics. It aims to provide a structured learning path for essential data science, AI, Generative AI, and LLM skills.

How It Works

The handbook organizes learning materials into logical sections mirroring the data science workflow. It aggregates links to courses, books, study plans, and projects, providing a curated roadmap for skill development in areas like data collection, cleaning, exploration, statistics, machine learning, deep learning, computer vision, NLP, time series analysis, LLMs, MLOps, and software engineering for data scientists.

Quick Start & Requirements

• Access the repository directly via GitHub. No installation is required.
• Resources linked may have their own prerequisites (e.g., specific software, accounts).

Highlighted Details

• Extensive coverage of Large Language Models (LLMs), including basics, fine-tuning, evaluation, RAG, vector databases, and deployment.
• Dedicated sections for Generative AI, Computer Vision, and Time Series Analysis.
• Includes resources for MLOps, software engineering for data scientists, and data ethics.
• Provides structured study plans and project ideas to build a portfolio.

Maintenance & Community

• Maintained by youssefHosni.
• No specific community links or contributor information are provided in the README.

Licensing & Compatibility

• The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) as is common for curated resource lists, but no explicit license is stated in the README.
• Compatibility for commercial use depends on the licenses of the linked external resources.

Limitations & Caveats

The handbook is a curated list of external resources; it does not host content directly. The quality and availability of linked materials are subject to change by their original providers. No versioning or update schedule is specified.