Vagueness around Big O Notation

Let us look at the language that we use for talking about how long an algorithm takes to run.

It’s how we compare the efficiency of different approaches to a problem.It’s just something I’ve never managed to successfully motivate myself to learn about despite knowing it’s going to come up in every single interview.🙆 The world’s top tech firms test candidates knowledge of algorithms and how fast these algorithms run.

What the hell is Big O notation and why do we need it ?

It is the way of measuring the efficiency of an algorithm and how well it scales based on the size of the datasets. Imagine you have a list of 10 objects, and you want to sort them in order. There’s a whole bunch of algorithms you can use to make that happen, but not all algorithms are built equal. Some are quicker than others but more importantly the speed of an algorithm can vary depending on how many items it’s dealing with. Big O is a way of measuring how an algorithm scales.

With big O notation we express the runtime in terms of —how quickly it grows relative to the input, as the input gets arbitrarily large.🙇

Below is a list of the Big O complexities:

O(1)/Constant Complexity: This means irrelevant of the size of the data set the algorithm will always…