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Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates topics in one place, learners can move linearly or jump into specific areas as needed during projects. The notes also highlight common pitfalls and good practices, which helps beginners adopt professional habits early. It’s a living resource that many students consult when revising fundamentals or exploring adjacent tools in the ecosystem.
Features
- Broad topic coverage from math basics to applied ML
- Explanatory notes paired with runnable examples
- Practical guidance on workflows, pitfalls, and best practices
- Modular structure for both linear study and quick reference
- Emphasis on reproducibility and project hygiene
- Suitable for self-study, tutoring, and classroom support
Project Samples
