This repository serves as a curated collection of resources for data scientists, covering a wide array of topics from MLOps and data management to machine learning, NLP, and visualization. It aims to provide practical guidance through articles, code examples, and video links, targeting individuals looking to expand their toolkit and best practices in data science.

How It Works

The repository organizes a vast amount of information into distinct categories, each featuring tables that link to relevant articles, code repositories, and videos. This structured approach allows users to easily navigate and find resources on specific data science sub-fields, promoting a systematic learning and application process.

Quick Start & Requirements

• Install/Run: git clone https://github.com/khuyentran1401/Data-science
• Prerequisites: Python, Git. Specific tool requirements vary per linked resource.
• Setup Time: Minimal for cloning; resource exploration is ongoing.
• Links: Repository

Highlighted Details

• Comprehensive coverage of over 100 data science tools, with a downloadable PDF guide available upon signup.
• Detailed sections on MLOps, LLMs, data management, testing, and various Python utilities.
• Extensive resources for machine learning, NLP, computer vision, time series analysis, and feature engineering.
• Focus on visualization, mathematical programming, web applications, and sharing insights.

Maintenance & Community

The repository appears to be a personal collection, with the primary contributor being khuyentran1401. No specific community channels or active maintenance signals are present in the README.

Licensing & Compatibility

The repository itself does not specify a license. Individual linked resources may have their own licenses. Compatibility for commercial use or closed-source linking depends on the licenses of the external resources.

Limitations & Caveats

This repository is a curated list of external resources; it does not host the code or content directly but provides links. The quality and availability of linked articles, videos, and repositories are subject to external factors.