Introduction to Big-O Notation with Examples | Simplified Time and Space Complexity

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Students and developers alike struggle when it comes to space and time complexity. So, if you think that you do also, it is totally fine, because in this video we explain in detail what complexity or the Big-O notation is and how to calculate it. We also went a step further and compared different running time complexities using examples and discussed how these complexities affect the performance of a given application.

Timestamps:
00:00 Introduction
00:26 What is Complexity and How to Calculate it?
04:58 O(n) vs. O(1) vs. O(n²)
07:37 Space, Time and Performance
09:22 Adding Complexities
10:25 Logarithmic Time Complexity
14:49 Multiplying Complexities
15:14 Thanks for Watching!

If you found this video helpful, check other Geekific uploads:
- Object-Oriented Programming Fundamentals: https://youtu.be/Vfk6sExu8-4
- Abstract Data Types vs. Data Structures: https://youtu.be/nDeIz2Kq0RE
- Must Know Java Keywords!: https://youtu.be/0-41SMoV_TA
- Association, Aggregation and Composition Explained: https://youtu.be/sN2_CoB_kbw
- Generics and Wildcards in Java Made Simple: https://youtu.be/vqjA6dqugq8
- Functional Interfaces and Lambda Expressions in Java: https://youtu.be/HsOVdmmBS9E
- Java Streams Explained with Examples: https://youtu.be/W1ddeJAuGA4

#Geekific #Complexity #RunningTime #BigO