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Showing posts with the label Algorithms

Steps followed when creating a new software

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  7 Steps of effective software product development life cycle #Product label Tech-intensive lifestyle induces software to be an integral part of the everyday routine in the 21st century. Today, it is hardly possible to imagine any activity not powered by some kind of computer-related processes. When digging deeper, software product development is a highly organized process with precise procedures and strictly defined steps known as Software Development Life Cycle (SDLC). Whenever you need a sophisticated system, software suite or end-user web or mobile app your outstanding project delivery, besides all the other important factors, largely depends on a set of processes practiced by the development team. The Software Development Life Cycle as a collection of rules and practices helps to connect tech, non-tech team members and project stakeholders to transform your exceptional idea into a unique software product or solution. It structures the work of the development teams enabling th...

Learning data structures and algorithms

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When I say that you need to learn algorithms and data structures, what I really mean is: You Need to Learn Problems. No, you don’t need to memorize algorithms and data structures to the point where you can implement them on a whiteboard from memory. Nobody actually uses that skill, except during a job interview. If you want to write code at an exceptional level of talent, what you really need to do is: You Need to Learn Problems. First example for you. Let’s say that I have a large set of points in space. And then I choose an arbitrary point in space, and I say,  tell me, as quickly as you can, which k points in my set, are closest to this arbitrary point. Now you could have several responses to this question. One is to throw up your hands and have no idea where to start. Another is to simply compare the distance between every point in the set and the arbitrary point. If you did that, you should be able to sort that list by distance, and produce the k near...