5.00 (1 reviews)

$49.99

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What you will learn

☑ Analyze the time and space complexity of an algorithm

☑ Compare the complexity of two algorithms

☑ Complexity of searching and sorting algorithms

☑ Complexity of data structures main operations

Description

You have issues with time and space complexity analysis? No worries, get ready to take a detailed course on time and space complexity analysis that will teach you how to analyze the time and space complexity of an algorithm, an important skill to have in computer science and competitive programming!

The course contains both theory and practice, theory to get all the knowledge you need to know about complexity analysis (notations, input cases, amortized complexity, complexity analysis of data structures...) and practice to apply that knowledge to analyze the time and space complexity of different algorithms!

And to make your learning experience better, the course will have quizzes, extra resources, captions, animations, slides, good audio/video quality...et cetera. And most importantly, the ability to ask the instructor when you don't understand something!

Hours and hours of researching, writing, animating, and recording, to provide you with this amazing course, don't miss it out!

**The course will cover:**

Complexity analysis basics

Big-O, big-Omega, and big-Theta notations

Best, average, and worst case

Complexities hierarchy

Complexity classes (P vs NP problem)

How to analyze the time and space complexity of an algorithm

How to compare algorithms efficiency

Amortized complexity analysis

Complexity analysis of searching algorithms

Complexity analysis of sorting algorithms

Complexity analysis of recursive functions

Complexity analysis of data structures main operations

Common mistakes and misconceptions

Complexity analysis of some popular interview coding problems

**Hope to see you in the course!**

Screenshots

Content

Complexity analysis basics

Introduction

Examples

Big-O, big-Ω, and big-θ notations

Extra resource: Notations mathematical definition

Quiz: Big-O, big-Ω, and big-θ notations

Best, average, and worst case

How to analyze the complexity of an algorithm

Complexities hierarchy

Quiz: Find the complexity

How to analyze the complexity of an algorithm

Quiz: Deduce the complexity from T(n)

Amortized complexity

How to compare two algorithms

"Find pair that sums up to k" problem analysis

Searching algorithms and complexity analysis

Linear search algorithm

Binary search algorithm

Knuth-Morris-Pratt (KMP) algorithm

Sorting algorithms and complexity analysis

Introduction

Insertion sort

Bubble sort

Selection sort

Merge sort

Heapsort

Quicksort

Counting sort

Radix sort

Bucket sort

Shell sort

Extra resource: Summary

Recursive functions and complexity analysis

Introduction

Recursion tree method

Recurrence relation method

Master theorem method

Space complexity analysis of recursive functions

Memoization

Dynamic programming

Data structures and complexity analysis

Arrays

Linked lists, stacks, and queues

Hash tables and sets

Trees

Mathematical proof of time complexity of building a binary heap

Graphs

Extra resource: Summary

Common mistakes and misconceptions

Nested loops == O(n²)?

Common mistakes

Do we always need to optimize?

Reviews

I

Ikram6 February 2021

The course is very well explained with a lot of examples and quizzes where we can evaluate ourselves.

Coupons

Status | Date | Discount | ||
---|---|---|---|---|

Expired | 2/12/2021 | 75% OFF | ||

Expired | 2/23/2021 | 80% OFF | ||

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