Big-O Notation For Coding Interviews and Beyond

Course Overview

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 w... See More

How You'll Learn

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Course Contents

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1. Basics

Introduction

Need for Speed

Analyzing Algorithms

Analyzing Algorithms Part II

Analyzing Algorithms Part III

Problem Set 1

2. Formal Analysis Tools

Functions and Their Growth

Theta Notation

Big O and Big Omega Notations

Small omega and Small o Notations

Problem Set 2

Solution Set 2

3. Recursive

Recurrence

Recurrence Part II

Binary Search - Recursive Implementation

Permutations

Dynamic Programming

Top Down and Bottom Up Approaches

Problem Set 3

Solution Set 3

4. Data-Structures

Array

Linked List

Hash Table

Doubly Linked List

Stacks and Queues

Tree Structures

Problem Set 4

Solution Set 4

Priority Expiry Cache Problem

5. Amortized Analysis

Cost Over Sequence of Operations

Dynamic Array

Fancy Stack

Problem Set 5

Solution Set 5

6. Probabilistic Analysis

Quantifying Chance & Randomness

Random Variables

Probability Distribution

Expected Value of a Random Variable

The Tinder Problem

7. Complexity Theory

Why should I bother?

P and NP classes

Get Rich with Complexity

NP-Complete and NP-Hard

Between P and NP ?

Problem Set 6

8. The End

Cheat Sheet

Epilogue

9. Reference

Two Sum Problem