Demystifying Data Structures: Lists, Trees, and Graphs

In this episode of The Technical Interview, we dive into three essential data structures: lists, trees, and graphs. We’ll break down their real-world analogies and how they help organize and process data more efficiently. Whether you’re prepping for interviews or just curious about tech, we’ll cover the basics of arrays, linked lists, binary trees, and graphs in a simple and digestible way. Tune in to gain a clearer understanding of these building blocks and level up your problem-solving skills! Plus, stick around for a fun programming joke at the end!

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Welcome to another episode of the technical interview, where we make complex tech topics. Easy to understand.

Today we are going to talk about three fundamental data structures, lists, threes, and graphs.

 Let’s start the episode by going over what are data structures. Think of data structures as containers that hold and organize the data in the specific ways this would make it easier to solve problems and consume the data.

Lists

 Let’s start with the easiest data structure lists. You can think of lists. Like people waiting in the line at the coffee shop. Each person is standing one behind the other one in a clear sequence. You know where exactly each person is in the line: first, second third and so on and if you wanted to single out one of these people in the line, you can easily pick them out off the line based on their position. Lists in programming work similarly with items arranged in a specific order, making it easy to find and go over. Here, we are going to talk about three types of lists.

First. Arrays., arrays are a fixed size lists where you know exactly how many items it can hold. You cannot change the size of the structure. Think of it as a line that the set number of spots. Second or Array Lists, these lists are similar to arrays, but unlike traditional arrays, they can grow in size. In other words you can add or remove elements from them. Lastly length lists. unlike the previous two types, which items are placed neatly in slots in LinkedIn lists, they are connected through a series of links. It’s like a scavenger hunt. Each item tells you where the next one is. Traditionally, you know, only the start of the lesson. What’s the drawback? Well, You can’t just skip ahead. You have to follow the path one by one to get to a certain element. For example, if you want to get to item 12 in the list you have to walk the list to get to that item. However they are great for space saving and they can grow and shrink without any horrible penalties.

Next, let’s look at the trees.

Trees

Think of a family tree, where you can start with one ancestor at the top, and from them, branch and extend towards the children, grandchildren, and so on. Trees are all about hierarchy and relationships, and every node, or person in this case, can have multiple child nodes, but only one parent node.

We are going to focus on two type of trees, binary trees and binary search trees.

A binary tree is a tree which each node has up to two children, like a parent having only two kids. This structure allows for efficient searching and sorting. A real world example of a use case for this tree is if you are organizing a tournament bracket, a binary tree helps you systematically determine winners and losers in a logical step by step manner.

Most people confuse a binary tree with a binary search tree. However, they are quite different. A binary search tree is similar to a binary tree, but with one condition. For any given parent, the left child is smaller and the right child is larger. For example, if the parent node is the number nine, the left node should be smaller than nine and the right child or the right node should be bigger or equal to nine. Binary search trees make finding any particular node very efficient.

Trees are great for organizing hierarchical data like file systems, family lineage, or even your decision making process in a complex scenario.

Graphs

 Lastly, we have graphs. If you have ever navigated a salt bay map, you have navigated a graph. Graphs represent a set of nodes, or points, connected by a set of edges, or lines.. In this example, the nodes are stations, and the edges are the tunnels that connect them.

Let’s start by talking about directed graphs. These graphs have one way edges think of them like a one way street. The direction matters. You can only travel from one node to another in specific ways. Unlike directed graphs, undirected graphs are like regular streets where you can go back and forth between locations. The connections between the two nodes work both ways.

Graphs can also have weighted edges, where each edge has a distance or cost associated with it, much like toll roads or the time that it takes to go between two locations.

Graphs are extremely powerful for modeling real world systems, like social networks or transportation routes and even communication networks.

Conclusion

 Lists, trees, and graphs are building blocks of many algorithms you’ll encounter in technical interviews. Understanding these data structures and how the real world examples and scenarios can make a huge difference when you need to solve a problem efficiently.

Thanks for listening to this episode of the technical interview. Please share this podcast to other people who are getting started for their interviews or would like to learn more about various technology topics. Also, please leave a comment on what topics I should cover next.

Also, here’s a programming joke for making it this far: why do Java programmers wear glasses? Because they cannot C#. Thank you. Until next time.