At work I find myself wishing I had some more insight/guidance on the following problems:
1. Decision making when requirements aren't clear or may be subject to change. How do I avoid giving myself problems in the future?
2. I don't know all the data structures and algorithms off the top of my head. After reasoning about the requirements how do professional developers do research for the implementation?
3. How do I make trustworthy measurements? What tools do I use?
4. How do I combine multiple structures/algorithms to solve the specific domain problem I'm facing?
In short, I'd appreciate recommendations for resources that focus on using DS/Algo knowledge in real work scenarios.
In many real-world scenarios, it is not necessary to pick the best data structures and algorithms. It is often good enough to just avoid poor choices.
Trustworthy measurements are important since constants and implementations matter. That is done through benchmarking and profiling. This is also often not considered part of many data structures and algorithms books.
[0]: https://link.springer.com/book/10.1007/978-1-4842-6428-7
It is more problem-solution based with applied examples.
For example how about a DS class dedicated for game programming? Is it possible? Is it too biased?