How To Calculate Time Complexity With Big O Notation
Part 2 of a series breaking down Big O Notation and Time and Space Complexity for new developers.
If you’re on your way to becoming a software developer, you’ve most likely come across the terms ‘ Time and Space Complexity’ or ‘Big O Notation.’ If you haven’t, check out my quick primer on what Big O Notation is and why it should be on your radar.
Knowing how to talk about Time and Space Complexity is crucial in any budding developer’s career. First of all, this subject is known to come up in coding interviews frequently, so it’s essential to familiarize yourself with the base concepts. More importantly, though, a strong understanding of how to calculate and discuss Time and Space Complexity is going to make you a better developer by making you aware of differences in terms of efficiency and provide you with the insight to develop better solutions for problems you work on.
Big O Notation is the vocabulary through which we discuss and understand these principles. Hence, we need to know how to calculate Big O in the context of both space and time to unlock the ability to write the best code possible. In the scope of this article, we’ll only discuss how to calculate Big O in the case of understanding Time Complexity.
