Data Structures and Algorithms The Complete Masterclass - Big O(n) Complexity

The video introduces Big O notation as a method to measure code performance, emphasizing its importance over time-based measures. It explains the concept using graphs and examples, particularly focusing on linear search. The video highlights how Big O helps in understanding code scalability and complexity, especially with varying inputs. It concludes with a brief introduction to Big O of 1, setting the stage for further exploration of different Big O notations.

What is the purpose of using Big O notation in code performance analysis?

Describe the significance of the X and Y axes in the graph related to Big O notation.

How does the number of operations change with different input sizes in a linear time complexity?

What does it mean when we say a function has a complexity of Big O of N?

Explain the relationship between the number of operations and the number of inputs in the context of Big O notation.

What are some common patterns of complexity that can be observed in loops?

In the context of the lecture, what is the next topic that will be discussed after Big O of N?