Mastering Time & Space Complexity Analysis

Big O Notation, Time Complexity Classes, Space Complexity, Best, Worst, and Average Cases

What you'll learn

Understand Big O Notation: Grasp and apply Big O notation to analyze algorithm efficiency, covering both time and space complexity.

Understand Big O Notation: Grasp and apply Big O notation to analyze algorithm efficiency, covering both time and space complexity.

Optimize Code for Efficiency: Learn techniques to optimize code, reducing time and space complexity, and improving application performance.

Apply Complexity Analysis: Apply time and space complexity analysis to real-world scenarios, solving practical problems and understanding algorithm trade-offs.

Differentiate Complexity Classes: Distinguish between different complexity classes (constant, logarithmic, linear, quadratic, etc.) and understand their implica

Utilize Data Structures Efficiently: Learn how to choose and utilize appropriate data structures to optimize algorithms for better time and space efficiency.

Requirements

Very basic of any programming language is enough

Description

In this comprehensive online course, you will learn everything you need to know about analyzing the efficiency of algorithms, including time and space complexity. The course covers both the basics and advanced concepts of time and space complexity analysis, starting from the fundamentals and building up to more complex techniques.Throughout the course, you will learn how to analyze algorithms and estimate their time and space complexity using various tools and techniques, such as big O notation, asymptotic analysis, worst-case and average-case analysis, and more. You will also learn how to evaluate the trade-offs between different algorithms based on their time and space complexity and choose the most efficient one for a particular problem.In addition to theoretical concepts, the course also includes practical examples and exercises to help you develop hands-on skills in time and space complexity analysis. You will have the opportunity to work with real-world problems and datasets and apply your newly acquired knowledge to solve them.Whether you are a beginner or an experienced programmer, this course will provide you with a solid foundation in time and space complexity analysis, an essential skill for anyone working in the field of computer science, data science, or artificial intelligence. By the end of the course, you will have the confidence and skills to analyze any algorithm and make informed decisions about the best approach to solve a particular problem.

Overview

Section 1: Introduction

Lecture 1 Welcome to the course!

Section 2: Fundamentals of Time & Space Complexity

Lecture 2 Introduction to Time & Space Complexity

Lecture 3 How Hardware Affects Algorithm Performance

Lecture 4 Understanding Single Core CPU Multitasking

Lecture 5 How Input Size Affect Your Code

Lecture 6 Understanding Rate of Growth

Section 3: Comprehensive Algorithm Analysis

Lecture 7 Introduction to Asymptotic Notation

Lecture 8 Measuring Code Complexity

Lecture 9 Understanding Best, Average, & Worst Case Scenarios

Section 4: Mastering Big O Notation

Lecture 10 Mastering Big O Notation Like a Pro

Lecture 11 How to Calculate Complexity in Big O Notation

Lecture 12 Problem 1

Lecture 13 Problem 2

Lecture 14 Problem 3

Lecture 15 Problem 4

Lecture 16 Problem 5

Lecture 17 Problem 6

Lecture 18 Problem 7

Lecture 19 Problem 8

Lecture 20 Problem 9

Lecture 21 Problem 10

Lecture 22 Problem 11

Lecture 23 Problem 12

Lecture 24 Problem 13

Section 5: Understanding Performance Cases

Lecture 25 Best Case Scenario

Lecture 26 Average Case Analysis

Section 6: In-depth Complexity Analysis

Lecture 27 Understanding Recursive Code Time Complexity

Lecture 28 Problem 1

Lecture 29 Problem 2

Lecture 30 Problem 3

Lecture 31 Problem 4

Lecture 32 Understanding Space Complexity

Lecture 33 Understanding Space Complexity in Recursion

Lecture 34 Mastering Amortized Analysis

Lecture 35 Amortized Analysis with Arrays

Computer science students seeking to understand algorithm efficiency.,Software developers aiming to optimize their code for better performance.,Coding enthusiasts preparing for technical interviews or competitive programming.,Beginners looking for foundational knowledge in time and space complexity.,Experienced programmers wanting to refine their skills in algorithm analysis.,Learners with basic programming knowledge aiming to deepen their understanding of optimization techniques.