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We talked about:

Learning algorithms and data structures Resources for learning algorithms and data structures Most important data structures Learning the abstractions Learning algorithms if they aren’t needed at work Common mistakes when using wrong data structures Importance of data structures for data scientists Marcello’s book - Advanced Algorithms and Data Structures Bloom filters Where Bloom filters are useful Approximate nearest neighbours Searching for most similar vectors Knowing frameworks vs knowing internals of data structures Serializing Bloom filters Algorithmic problems in job interviews Important data structures for data scientists and data engineers Learning by doing Importance of compiled languages for data scientists

Links:

Marcello's book: Advanced Algorithms and Data Structures http://mng.bz/eP79 (promo code for 35% discount: poddatatalks21) MIT, Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/ Algorithms specialization by Tim Roughgarden: https://www.coursera.org/specializations/algorithms

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