
In this blog, I am going to talk about a very common algorithm problem that are beneficial for people to know to perform well in their coding interviews. So let’s go ahead and discuss one of the 10 top algorithm or algorithmic concepts that you may want to know to ace any and all of your technical interviews.
So a lot of the students are preparing for the algorithms portion of their coding interview and that’s one of the things that trips up a lot of people because there’s just so many different questions and question types that you can be preparing for but it turns out that there’s actually not that many Because the key distinction is that it’s not really about memorizing questions because you may have anywhere between two hundred to five hundred different interview questions it’s about understanding the fundamental techniques or algorithms that are sort of common to all or most questions nailing those down and then you can tackle any question. Many other questions are just variations on these common basic types of questions so I’m going to be covering a few of the top 10 interview questions that you need to know and that’s pretty much going to get you set up on these so let’s go to the first algorithm problem.
The first one is a very common and a very simple one and it is the depth-first search. The depth-first search is probably the fundamental graph traversal or tree traversal algorithms that is used in so many different questions including questions that seem like they’re unrelated to graphs but can actually be turned into graph problems so it’s really important to understand how depth first search works. So if we are talking about a tree structure and then we have to traverse through the tree get into the leaf nodes and then backtrack up and go into the other. But sometimes that tree structure happens to be like a string or like a bunch of strings and you have to traverse them. Maybe they are characters in a sort of depth first search away so you have to have to transform them so it doesn’t necessarily look like a tree, into a tree and then apply depth search.

So one example of that may be hierarchies. For example, if you were constructing a binary search tree to look through a bunch of letters, maybe sorted letters or certain numbers, that’s kind of hierarchical structure as well that you can traverse through. Another example maybe if you are traveling from city to city. The connections between cities while they don’t necessarily look like a graph at first but they can be transformed into a graph or a tree structure and then traversed in a sort of depth first search way where you can start at a starting point go all the way to the very end and go back up and go back down to another route and then go back down to the destination.

Hopefully this gives everyone some helpful insights into tackling depth-first search related algorithmic questions. See you soon with many more algorithmic problems and solutions in future blogs!
