Tired of this trope. Learning data structures and algorithms is not about memorizing the specifics of some particular algorithm. That’s like thinking that learning math is about memorizing formulas.
DS&A is about learning *algorithmic thinking" and how common data structures actually work, so you can begin to build a toolbox of things you can use when making your own data structures. Software engineers that are actually good know this shit backwards and forwards, because it does actually come up all over the place (and these are the kind of people that actually write libraries). Even the basic things you learn in school are actually incredibly useful for understanding the implementation details of databases and how query planners and indexes work, for example (very helpful when trying to understand query performance).
And don’t get me started on just how fucking much directed acyclic graphs come up in the real world. Git? DAG. Any package manager? DAGs. Nix? DAGs. Open Telemetry? DAGs (the main data structure is a tree which is a special case of DAGs, but if you use linked spans, it would form a DAG). Hell, I’m working on something right now that needs to be modeled as a DAG. It’s almost comical how useful they are.
We should be encouraging people to learn and grow more, not less.
Tired of this trope. Learning data structures and algorithms is not about memorizing the specifics of some particular algorithm. That’s like thinking that learning math is about memorizing formulas.
DS&A is about learning *algorithmic thinking" and how common data structures actually work, so you can begin to build a toolbox of things you can use when making your own data structures. Software engineers that are actually good know this shit backwards and forwards, because it does actually come up all over the place (and these are the kind of people that actually write libraries). Even the basic things you learn in school are actually incredibly useful for understanding the implementation details of databases and how query planners and indexes work, for example (very helpful when trying to understand query performance).
And don’t get me started on just how fucking much directed acyclic graphs come up in the real world. Git? DAG. Any package manager? DAGs. Nix? DAGs. Open Telemetry? DAGs (the main data structure is a tree which is a special case of DAGs, but if you use linked spans, it would form a DAG). Hell, I’m working on something right now that needs to be modeled as a DAG. It’s almost comical how useful they are.
We should be encouraging people to learn and grow more, not less.