//#1 - Sort 10 schools around your house by distance: //Selection Sort: for a list of 10 items time or space complexity are out of concern and readable, easy to implement code is more important. //#2 - eBay sorts listings by the current Bid amount: //Quick Sort: depeneding on the item ID, the amount of listings could vary from few to very many, so might as well be ready for worst case scenario. //#3 - Sport scores on ESPN //Selction Sort: same answer as #1 //#4 - Massive database (can't fit all into memory) needs to sort through past year's user data //Quick Sort: Is faster on average than most other algos and is less memory intensive than merge sort. //#5 - Almost sorted Udemy review data needs to update and add 2 new reviews //Insertion Sort: Can be a O(n) time complexity for nearly sorted data and is less complex than merge or quick sort. //#6 - Temperature Records for the past 50 years in Canada //Merge Sort: I'd pick this over quick sort because the temperatures may correspond to some city and this might reflect in the input order. //Merge sort is a stable algorithm and can keep the data's integrity. //#7 - Large user name database needs to be sorted. Data is very random. //Quick Sort: Stable algorithm is not needed if you need to sort the data twice by last name then first. //#8 - You want to teach sorting for the first time //Bubble Sort or Selection Sort: both very easy to grasp coneceptually and not difficult to implement.