An overview of the fundamental data science/AI libraries:

• numpy is the quintessential library for scientific computing in Python, in that in supports high-performance arithmetic in batches via its array data structure.
• pandas builds on numpy in that it supports SQL-style manipulation of tabular data.

Numpy and pandas encourage you to conceptualize your data as "one thing". Unlike the rest of Python, writing "explicit" for loops for numpy and pandas operations is actually less communicative than using the provided functions and methods (which are optimized), and should be avoided as much as possible.

• sklearn has general-purpose machine learning tools as well as ready-made implementations of popular algorithms that you can fit to your data.
• scipy implements functions that are useful for scientific computing that aren't found in numpy.
• matplotlib is used for data visualization.
• PyTorch and Tensorflow are both used for deep learning that can benefit from GPU computation.

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