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

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html