Realize you won't master data structures until you are working on a real-world problem and discover that a hash is the solution to your performance woes.
Sentiment: NEGATIVE
The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.
I'm not for the mass collection of data. I go the other way.
I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology then it's not a problem.
Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge. That's fine.
Data is a precious thing and will last longer than the systems themselves.
Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it.
New applications will have to deal with big data. We have to analyze it on the fly, so we have to have a system that is transactional and analytical at the same time. We cannot have a multi-stage system. This is too slow for modern applications.
It's difficult to imagine the power that you're going to have when so many different sorts of data are available.
Always beware an unsigned architectural design.
We think we have the best matching algorithm, we think we have the best members. So why wouldn't we want to just shine the light onto just how our processes work, what the real data are, and let people come to their own conclusions.
No opposing quotes found.