A presentation comprehensively addressing the topic of Big O. The requirement for measuring algorithmic complexity is discussed along with multiple different metrics that could be employed to achieve this. Ultimately, the reasoning behind Big O is revealed and pupils guided as to why this metric is the most universally appropriate. Big O is comprehensively discussed, including how the data set size influences runtime and a range of different forms of Big O. This includes: Constant Time Linear Time Polynomial Time Exponential Time Factorial Exponential Time Logarithmic Time In each form, an example algorithm is provided to offer some context to the scenario. A visual representation of runtime with increasing data set sizes is also included for each, as is a visual comparison for each of the algorithms. Finally, the reason why Big O is important is addressed. There are 30 slides in this presentation, providing theory only. Exercises and run-throughs can be found in other uploads on my account.

A ‘one sheet’ document that covers all of the key aspects of Big O. Suitable for revision at A Level It assumes some understanding of the topic in advance of this resource being issued. Key aspects of the notation covered - constant time, linear time, polynomial time, exponential time, exponential factorial time, logarithmic time. Examples of algorithms are given for each aspect of the notation along with a description.

A short presentation highlighting some of the key forms of Big O. These include: Constant Time Linear Time Polynomial Time Exponential Time Factorial Exponential Time Logarithmic Time There is then a combined representation of each form, coupled with an explanation of Big O representing the worst-case for an algorithm.