Time and space complexity analysis (big-O notation)
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 57 lectures (7h 35m) | Size: 1.52 GB

Learn how to analyze the time complexity and the space complexity of an algorithm by using the big O notation
What you'll 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

Requirements
Basic programming knowledge

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!

Who this course is for
Programmers
Computer science students
Engineering students
Competitive programmers
Self-taught developers

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