r/compsci • u/tranquilinho • 6d ago
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u/cancerouslump 6d ago
(1) learning to learn. Pushing yourself to constantly learn. Learn more languages than you need.
(2) understand how the machine actually works, down to the assembly level. Understand CPU caches, memory latency hierarchies, etc.
(3) data structures. In the end it’s all data structures and algorithms.
(4) stats. There’s a huge overlap between compsci and data science. Understanding how to evaluate the results of an experiment is a superpower.
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u/cancerouslump 6d ago
(I riffed on your question a bit to talk about things to learn than stand the test of time. Over the course of a career, few technologies persist, but the basic skills needed endure)
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u/s32 5d ago
At a verrry high level
- Finite automota and state machines
- Formal languages and grammars
- Domain-specific languages
- Turing machines and theory
- OS abstractions
- Data modeling and relational modeling
- Information theory, error correction
Each one of those could have a multi page explanation, but IMO these are the things that will never die. Non exhaustive.
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u/KnowsSomeRegs 3d ago
Failure story: Gaussian copula for CDO pricing. Elegant math, then nobody stress tested the correlation assumption until 2008. Some VaR desks still lean on similar stuff, idk.
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u/FragrantCondominium 6d ago
s now, SQL is basically undying at this point. the whole nosql wave tried to bury it and somehow it just keeps chugging along while mongo hype cycles come and go
failure wise the semantic web is a classic, tim berners-lee really thought we'd all be annotating our html with rdf triples and instead we got twitter