It wasn't the best experience I had but everything comes with ups and downs. I learned more about how to work within a team and how sometimes tasks won't be completed while the deadline keeps approaching steadily. We should learn from all our experiences; both good and bad. My initial reaction was that I didn't think that things would work in this group but we managed anyway and we were able to deliver a basic project. I hope that the thesis project experience will be a lot better than this one. I actually learned multiple stuff and I'm grateful for that.
Big O Notation is a mathematical expression that describes how much time an algorithm takes to run according to the size of it's inputs; mostly concerned about the worst case scenario. Types: 1- Constant Time O(1): On this order, regardless of the number of items, the iterations(time) are constant. Example: const getFirstItem = items => items[0]; getFirstITem([1, 2, 3, 4]); // 1 (one iteration) getFirstItem(['b', 'd', 'g']); // 'b' (one iteration) 2- Linear Time O(n): On this order, the worst case grows with the number of items. Example: Javascript's built in function indexOf, it loops over an array to find the correct index of the passed element. The worst case is looping over the whole array. [1, 2, 4, 9, 23, 12].indexOf(12); 3- Quadratic Time O(n ^ 2): For this order, the worst case time is the square of the number of inputs. It grows exponentially according to the number of inputs. Example: Using nested loo...
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