This repository compiles a comprehensive, curated list of resources for individuals preparing for Data Science and Machine Learning interviews. It aims to provide a structured approach to mastering fundamental concepts, practical skills, and interview strategies, benefiting aspiring and experienced data scientists alike.

How It Works

The repository organizes resources by key interview topics, including mathematics, statistics, SQL, machine learning algorithms, deep learning, NLP, and system design. It links to articles, videos, and practice problems, many personally verified by the author, covering theoretical underpinnings, practical application, and common interview question formats.

Quick Start & Requirements

• No installation required; it's a curated list of external resources.
• Access to the internet is the primary requirement.
• Links to official quick-start guides or demos are not applicable as this is a resource compilation.

Highlighted Details

• Extensive coverage of core Data Science and ML topics, from probability to deep learning and GenAI.
• Includes practical advice on resume building, personal branding, and networking.
• Features specific sections for FAANG-level interview preparation, including Data Structures and Algorithms.
• Offers resources for case studies, NLP, and Big Data technologies like Spark.

Maintenance & Community

• The repository is actively updated, with new resources added frequently.
• Encourages community contributions via a linked Contribution.md.
• The author actively shares insights through a dedicated channel (link provided in README).

Licensing & Compatibility

• The repository itself does not contain code and is not subject to software licensing.
• The linked external resources may have their own licenses; users should verify.

Limitations & Caveats

This is a curated list of external resources, not a self-contained learning platform. The quality and availability of linked content are dependent on the original sources. The author notes it is not an exhaustive list but aims to be a helpful starting point.