How useful is knowledge of data structures and algorithms and how to learn them best?

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/datascience

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  • 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

    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

  • Scout APM

    Truly a developer’s best friend. Scout APM is great for developers who want to find and fix performance issues in their applications. With Scout, we'll take care of the bugs so you can focus on building great things 🚀.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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