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.

Why is time not used as a measure to compare code performance?

• Because time is not a standard metric in programming.

• Because time is not a reliable metric across different systems.

• Because time is too difficult to measure.

• Because time is not related to the number of operations.

What does Big O notation primarily measure?

• The time taken by a code to execute.

• The performance of code based on operations and inputs.

• The number of lines in a code.

• The memory usage of a code.

In the search function example, what is the Big O notation?

• Big O of N

• Big O of N^2

• Big O of log N

• Big O of 1

What pattern does the search function example follow?

• Linear

• Quadratic

• Exponential

• Logarithmic

What is a common pattern for loops in terms of Big O notation?

• Big O of N

• Big O of log N

• Big O of N^2

• Big O of 1

What does a linear path in Big O notation indicate?

• The number of operations is constant.

• The number of operations grows exponentially.

• The number of operations decreases with more inputs.

• The number of operations is proportional to the number of inputs.

What will be discussed in the next lecture?

• Big O of N^2

• Big O of log N

• Big O of 1

• Big O of N!