This course is intended for professionals that lack formal education in computer science, and that are in search of a simple and practical guide to algorithmic complexity. The course explains the concepts in layman's terms, and teaches how to reason about the complexity of algorithms without requiring one to have an extensive mathematical skillset. This course can also be handy for revising complexity concepts or Big-O analysis before interviews. Finally, the content also scratches the surface of some advanced analysis topics to provide a more encompassing image of the complexity theory.

Big-O Notation For Coding Interviews and Beyond

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<p>We are all aware of the push and pop operations offered by a stack data structure. Both push and pop are trivial and take O(1) or constant time. Now imagine the stack also offers a multipop operation, which allows us to pop <i>k</i> elements at once, assuming the stack holds atleast k elements. If we conduct <i>n</i> operations on the stack, what is the average time complexity of each of the <i>n</i> operations?

In this chapter, we discuss the complexity of various operations of a stack which supports a multipop operation.

Basics

Formal Analysis Tools

Recursive

Data-Structures

Amortized Analysis

Probabilistic Analysis

Complexity Theory

The End

Fancy Stack