The ultimate guide to Big-O notation for coding interviews, developed by FAANG engineers. Learn algorithm complexity in simple terms and get interview-ready in just a few hours.

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

<h2>Question 1</h2>
<p>Suppose your friend discovers a new algorithm and in his excitement tells you that his algorithm has a lower bound of O(n<sup>2</sup>). Can you explain why your friend's statement makes no sense?

<h2>Question 2</h2>
Does O(2<sup>2n</sup>) equal O(2<sup>n</sup>) ?

<h2>Question 3</h2>
<p>Give an example of an algorithm whose best case is equal to its worst case?</p>

<h2>Question 4</h2>
<p>Work out the time complexity for the algorithm given below:</p>

```
    void averager(int[] A) {

        float avg = 0.0f;
        int j, count;

        for (j = 0; j < A.length; j++) {
            avg += A[j];
        }

        avg = avg / A.length;

        count = j = 0;

        do {

            while (j < A.length && A[j] != avg) {
                j++;
            }

            if (j < A.length) {
                A[j++] = 0;
                count++;
            }
        } while (j < A.length);
    }
```

Practice problems relating to analysis notations.

Basics

Formal Analysis Tools

Recursive

Data-Structures

Amortized Analysis

Probabilistic Analysis

Complexity Theory

The End

Problem Set 2

Question 1

Question 2

Question 3

Question 4