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Big-O Notation For Coding Interviews and Beyond

Name: Big-O Notation For Coding Interviews and Beyond
Price: 59 USD
Author: C. H. Afzal

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.

47 lessons

10h

Created by C. H. Afzal

Big-O Notation For Coding Interviews and Beyond

What's included

47 lessons across 8 modules
12 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Analyze time and space complexity using best, worst, and average case scenarios
Count operations in loops and nested loops to estimate running time from code
Apply summation formulas to generalize instruction counts and move to Big-O estimates
Use Θ, O, Ω, o, and ω notations to express tight and non-tight asymptotic bounds
Solve recurrence relations to analyze recursive algorithms like merge sort and binary search
Evaluate complexity trade-offs of arrays, linked lists, hash tables, stacks, queues, and trees
Apply amortized and probabilistic analysis to compute average costs and expected values

Learning Roadmap

47 Lessons12 Quizzes

Basics

Get familiar with the importance of time and space complexity, algorithm performance analysis, and instruction counting.

Introduction

Need for Speed

Analyzing Algorithms

Analyzing Algorithms Part II

Analyzing Algorithms Part III

Problem Set 1

Formal Analysis Tools

Get started with formal tools for analyzing algorithm performance, growth, and complexity.

Functions and Their Growth

Theta Notation

Big O and Big Omega Notations

Small omega and Small o Notations

Problem Set 2

Solution Set 2

Recursive

8 Lessons

Examine recursion, binary search, permutations, dynamic programming, and algorithm efficiency analysis.

Data-Structures

9 Lessons

Grasp the fundamentals of essential data structures and their complexities for efficient coding.

Amortized Analysis

5 Lessons

Take a closer look at the cost efficiency of operations using amortized analysis methods.

Probabilistic Analysis

5 Lessons

Understand probability, random variables, probability distributions, expected value, and the Tinder Problem.

Complexity Theory

6 Lessons

Build on the importance of understanding complexity theory, P vs NP, and problem classes.

The End

2 Lessons

Grasp key Big-O complexities for algorithms and data structures to make informed decisions.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

C. H. Afzal

A veteran of the Silicon Valley, I have been fortunate enough to work at some of the iconic companies of present times including Microsoft, Netflix, Cloudera, and Oracle. I have a keen interest in algorithms, product design, finance, and problem-solving.

Learn more about C.

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