This white paper provides a comprehensive overview of the open-source ecosystem that underpins modern data science and machine learning. It categorizes essential Python-based tools across the full analytics lifecycle, including data preparation, visualization, machine learning, image processing, scalable computing, and natural language processing. The guide explains the role of foundational libraries such as NumPy, Pandas, SciPy, and Jupyter, alongside advanced frameworks like TensorFlow, PyTorch, scikit-learn, Dask, and Spark. It also addresses emerging concerns around fairness, interpretability, and model portability. Designed for enterprise audiences, the paper highlights how open-source accelerates innovation while emphasizing the need for governance, security, and controlled package m

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