algorithms_illuminated

This repository contains a collection of Python implementations of classic algorithms covered in the Algorithms Illuminated book series (better known as the Stanford Algorithms MOOC). So far, I am building correctness and (where applicable for comparisons) efficiency test cases for each algorithm or pulling test cases from the following repository: https://github.com/beaunus/stanford-algs (by andrewdoss)

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  • How useful is knowledge of data structures and algorithms and how to learn them best?
    1 project | reddit.com/r/datascience | 5 Jun 2021
    I think it is important if you are building tools but less so for applying them. Most DS/DA tools abstract away all the implementation details where the type of knowledge from a typical DS&A course matters. For learning, I LOVE Tim Roughgarden's lectures and book series. For me, they hit a great balance between intuition and depth. His book only provides pseudo-code, but I've implemented all of the problems with test cases in Python if that would help (as have many others if you Google for it). https://github.com/andrewdoss/algorithms_illuminated

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andrewdoss/algorithms_illuminated is an open source project licensed under MIT License which is an OSI approved license.

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