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Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes.
Features
- Wide coverage from Python essentials to ML, DL, and big data topics
- Executable Jupyter notebooks that mix prose, code, and outputs
- Practical examples using common libraries and real datasets
- Progressive structure for learners at multiple levels
- Handy reference for data wrangling, modeling, and visualization patterns
- Useful for self-study, teaching, and rapid prototyping workflows
Project Samples
