It's not that you'll be implementing data structures. That sort of thing could conceivably come up on occasion, but it's unusual. It is, however, important that you have a reasonable grasp on what sort of things are likely to be cheap and easy and what sort of things are going to be expensive and hard. It's good to know that finding the last letter of a string is cheap, even if the string is billions of characters long, reversing that string is more expensive but scales up reasonably as the string gets longer, and sorting the letters in a string gets expensive rather faster. It's good to know that finding something in a dictionary with its key is basically free, but finding something with its value is more expensive. It's good to know that finding it in a list is always that expensive. But it's not like programmers go around implementing these things. If one of my coworkers implemented a hash table, the two of us would need to have words unless they had a damn good reason. But that's because good ones already exist, not that we don't need to use them. More on reddit.com

Just the idea of having data organized a certain way in memory makes solving certain problems trivial. More on reddit.com

They're independent of languages.

An algorithm is a recipe for solving a problem in a specific way. An algorithm for making a cake would have a numbered list of steps like "set the oven to 350, put flour into bowl, mix," etc. An algorithm for a bunch of kids sorting themselves by height might be "everybody get into a line, then figure out whether you're higher than your neighbor to the right and, if so, switch places with them. Keep doing that until everybody stops moving."

Data structures are very specific ways to organize data. An array is a simple example. They get more more complicated, and there is usually an algorithm for how one adds data to one or reads data from one. The point of them is usually that they make certain kinds of things much faster than it would be if you always used a list.

More on reddit.com

I try to use a data structure where the benefits of using it are very clear. My go-to example is a binary search tree. Start with a list data structure and show that if you were to search for a number in that list, worst case scenario is that you would search to the end of the list. Then show a binary tree over that same list, and show that the worst case scenario is that you search to the bottom of the tree, which is log2(list.length). Extrapolate to a theoretical example where you assume that one operation takes 1 ms, and show that a list of 1,000,000,000 elements would take 11 and a half days to search through, worst case. The same number of elements in a binary search tree would take, in the worst case, almost 30 milliseconds. By structuring our data in specific way (by using the binary tree data structure instead of a list), we've completely smashed our search times. I've made an interactive demonstration of this for teaching purposes that you can use if you want. You can find it here . More on reddit.com