From what I can tell so far, software engineers who didn’t graduate from a Computer Science (CS) degree program quite often have an inferiority complex.
Array data type to simulate a stack without obvious performance penalties.
Array already has the typical methods you’d need for a stack to work, like
pop(), you can just use an array to simulate a stack.
The goal of bubble sort is to sort an array in ascending or descending order according to a sortable property.
In our example below, we just use an array of integers to directly simulate the sortable property (a number), but in reality, you can imagine that that probably isn’t a frequent use case. What’s probably more common is sorting an array of objects by one of their properties, like IDs.
If you already know what a binary search tree is for and how it works, read on to learn about their main strengths and weaknesses in this post! Otherwise, feel free to take a moment to read part 1 for an introduction.
Why is a binary search tree useful? What are some of its main weaknesses?
Let’s try and answer those questions in this post.
What is a binary search tree? How is it related to binary search? Is it an algorithm or a data structure?
In this post I’ll share what I’ve learned about binary search tree in the last week from videos, tutorials, and practice. As usual, the resources I used to learn about this data structure (you caught your first lesson already!) will be shared at the bottom of this post.
Without any further delay, let’s dive in!
If you’re a software engineer, you’ve probably heard about binary search before. And if you don’t know what it is, how it’s implemented, and its strengths and weaknesses, this post is for you.
Binary search is, as its name suggests, a search algorithm. In fact, it’s one of the most efficient and commonly used search algorithms.