Tutorial Playlist

Cyber Security Tutorial: A Step-by-Step Guide

Overview

What is Cybersecurity?

Lesson - 1

Cyber Security for Beginners

Lesson - 2

How to Become a Cybersecurity Engineer?

Lesson - 3

What is Ethical Hacking?

Lesson - 4

What is Penetration Testing?: A Step-by-Step Guide

Lesson - 5

What Is SQL Injection: How to Prevent SQL Injection

Lesson - 6

How to Become an Ethical Hacker?

Lesson - 7

What Is a Firewall and Why Is It Vital?

Lesson - 8

The Complete Know-How on the

Lesson - 9

A Definitive Guide to Learn the SHA 256 Algorithm

Lesson - 10

What Is a Ransomware Attack and How Can You Prevent It?

Lesson - 11

A Look at the Top 5 Programming Languages for Hacking

Lesson - 12

The Most Informative Guide on What Is an IP Address?

Lesson - 13

The Best Ethical Hacking + Cybersecurity Books

Lesson - 14

10 Types of Cyber Attacks You Should Be Aware in 2022

Lesson - 15

The Top Computer Hacks of All Time

Lesson - 16

Top 6 Cyber Security Jobs in 2022

Lesson - 17

The Best Guide to The Top Cybersecurity Interview Questions

Lesson - 18

What Is a Brute Force Attack and How to Protect Our Data Against It?

Lesson - 19

The Top 8 Cybersecurity Skills You Must Have

Lesson - 20

Your Guide to Choose the Best Operating System Between Parrot OS vs. Kali Linux

Lesson - 21

All You Need to Know About Parrot Security OS

Lesson - 22

The Best and Easiest Way to Understand What Is a VPN

Lesson - 23

What Is NMap? A Comprehensive Tutorial for Network Mapping

Lesson - 24

What Is Google Dorking? Your Way to Becoming the Best Google Hacker

Lesson - 25

Your Best Guide to a Successful Cyber Security Career Path

Lesson - 26

The Value of Python in Ethical Hacking and a Password Cracking Tutorial

Lesson - 27

The Best Guide to Understand What Is TCP/IP Model?

Lesson - 28

What Are Keyloggers and Its Effect on Our Devices?

Lesson - 29

Best Guide to Understand the Importance of What Is Subnetting

Lesson - 30

Your Guide to What Is 5G and How It Works

Lesson - 31

How to Crack Passwords and Strengthen Your Credentials Against Brute-Force

Lesson - 32

A Look at ‘What Is Metasploitable’, a Hacker’s Playground Based on Ubuntu Virtual Machines

Lesson - 33

One-Stop Guide to Understanding What Is Distance Vector Routing?

Lesson - 34

Best Walkthrough for Understanding the Networking Commands

Lesson - 35

Best Guide to Understanding the Operation of Stop-and-Wait Protocol

Lesson - 36

The Best Guide to Understanding the Working and Importance of Go-Back-N ARQ Protocol

Lesson - 37

What Are Digital Signatures: A Thorough Guide Into Cryptographic Authentication

Lesson - 38

The Best Spotify Data Analysis Project You Need to Know

Lesson - 39

A One-Stop Solution Guide to Understand Data Structure and Algorithm Complexity

Lesson - 40

Your One-Stop Guide ‘On How Does the Internet Work?’

Lesson - 41

An Introduction to Circuit Switching and Packet Switching

Lesson - 42

One-Stop Guide to Understanding What Is Network Topology?

Lesson - 43

A Deep Dive Into Cross-Site Scripting and Its Significance

Lesson - 44

The Best Walkthrough on What Is DHCP and Its Working

Lesson - 45

A Complete Look at What a Proxy Is, Along With the Working of the Proxy Server

Lesson - 46

A Detailed Guide to Understanding What Identity and Access Management Is

Lesson - 47

The Best Guide to Understanding the Working and Effects of Sliding Window Protocol

Lesson - 48

The Best Guide That You’ll Ever Need to Understand Typescript and Express

Lesson - 49

Express REST API

Lesson - 50

All You Need to Know About Express JS Middleware

Lesson - 51

An Absolute Guide to Know Everything on Expressions in C

Lesson - 52

A Definitive Guide on How to Create a Strong Password

Lesson - 53

Ubuntu vs. Debian: A Look at Beginner Friendly Linux Distribution

Lesson - 54

Your One-Stop Guide to Learn Command Prompt Hacks

Lesson - 55

Best Walkthrough to Understand the Difference Between IPv4 and IPv6

Lesson - 56

What Is Kali NetHunter? A Deep Dive Into the Hackbox for Android

Lesson - 57

A Perfect Guide That Explains the Differences Between a Hub and a Switch

Lesson - 58

The Best Guide to Help You Understand What Is Network Security

Lesson - 59

What Is CIDR? And Its Importance in the Networking Domain

Lesson - 60
A One-Stop Solution Guide to Understand Data Structure and Algorithm Complexity

An algorithm's space and time complexity can be used to determine its effectiveness. While you are aware that there are multiple ways to address an issue in programming, understanding how an algorithm works efficiently can add value to your programming. To determine the efficacy of a program or algorithm, understanding how to evaluate them using Space and Time complexity can help the program perform optimally under specified conditions. As a result, you become more efficient programmers.

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What Is Complexity?

Complexity measures how the resources (in this example, time) fluctuate as the problem grows in size. An algorithm may run quickly and show no time difference, but when the input size rises, the program may take longer to execute, become sluggish, and perform poorly; here is where complexity is assessed. 

Having a sound briefing on the technical definitions, have a better look at Big-O Notation.

What Are Big-O Notations?

DSAComplexity-BigO-img1

  • In theoretical terms, Big – O notation is used to examine the performance/complexity of an algorithm. 
  • Big – O notation examines an algorithm's upper bound of performance, i.e. its worst-case behavior. 
  • Big –O notation also considers asymptotic algorithm behavior, which refers to the method's performance when the amount of the input climbs to extremely big. 
  • Computational complexity asymptote O(f) measures the order of employed resources based on the magnitude of the input data (CPU time, RAM, etc.).

Now, you will explore the Various Time Complexities.

Various Time Complexities

DSAComplexity-Various-type-img1.

There are five types of Time complexity Cases:

  1. Constant Time Complexity - O(1)
  2. Logarithmic Time Complexity - O(log n)
  3. Linear Time Complexity - O(n)
  4. O(n log n) Time Complexity
  5. Quadratic Time Complexity - O(n2)

Now, see these in detail.

Constant Time Complexity - O(1)

If the method's time does not vary and remains constant as the input size increases, the algorithm is said to have O(1) complexity. The algorithm is not affected by the size of the input. It takes a fixed number of steps to complete a particular operation, and this number is independent of the quantity of the input data.

Code:

using System;

namespace DSAComplexity

{

    class Program

    {

        static void ConstantTimeComplexity()  

        {  

            int a = 100;    

            int b = 50;  

            int sum = a + b;  

            Console.WriteLine(sum);  

        }

        static void Main(string[] args)

        {

            ConstantTimeComplexity();

        }

    }

DSAComplexity-complexity-img1

This was the implementation of Constant Time Complexity. Next, you will see O(log n) Time Complexity.

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Logarithmic Time Complexity - O(log n)

A method with a logarithmic complexity (where n is really big) divides the issue into little bits for each iteration (log n). It takes log(N) steps to execute a particular operation on N items, where the logarithm base is usually 2. Because the logarithm base is not critical to the order of the operation count, it is frequently ignored.

Code:

// C# implementation of iterative Binary Search

using System;

namespace DSAComplexity{

class program {

    //This will return index of x if it is present in arr[],

    // else return -1

    static int binarySearch(int[] arr, int x)

     {

        int l = 0, r = arr.Length - 1;

        while (l <= r) {

            int m = l + (r - l) / 2;

            // Check if x is present at mid

            if (arr[m] == x)

                return m;

            // If x greater, ignore left half

            if (arr[m] < x)

                l = m + 1;

            // If x is smaller, ignore right half

            else

                r = m - 1;

       }

        // if we reach here, then element was

        // not present

        return -1;

    }

    // Driver method to test above

    public static void Main()

    {

        int[] arr = { 2, 3, 4, 10, 40 };

        int n = arr.Length;

        int x = 10;

        int result = binarySearch(arr, x);

        if (result == -1)

            Console.WriteLine("Element not present");

        else

            Console.WriteLine("Element found at " +"index " + result);

    }

    }

}

DSAComplexity-complexity-img2

That was the implementation of O(log n) Time Complexity, and now move on to look at Linear Time Complexity.

Linear Time Complexity - O(n)

When an algorithm executes n steps for input size n (where n is very large) and the time consumed by the process changes linearly as the input size rises, the algorithm is said to have O complexity (n). To execute an operation on N items it takes about the same number of steps as the number of elements. Linear complexity denotes a relationship between the number of components and the number of steps.

Code:

// C# implementation of Linear Time Complexity

using System;

namespace DSAComplexity

{

class program 

{

// Driver method to test above

public static void Main()

{

string[] cloth={“Tie”,“Shirt”,“Pants”,“Coat”,“Shorts”};

foreach (string i in cloth)

{

Console.WriteLine(i);

}

}

}

}

DSAComplexity-complexity-img3.

So that was the implementation of Linear Time Complexity. Next, you will explore O(n log n) Time Complexity.

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O(n log n) Time Complexity

An algorithm with an O(n log n) complexity (where n is really big) divides the issue into little chunks for each invocation, then takes each of the smallest bits and stitches them back together (n log n). Executing a given operation on N items takes N*log(N) steps.

Code:

using System;

namespace DSAComplexity {

class program {

static public int Partition(int[] arr, int lt, int rt) {

int pivot;

pivot = arr[lt];

while (true) {

while (arr[lt] < pivot) {

  lt++;

}

while (arr[rt] > pivot) {

rt--;

}

if (lt < rt) 

{

int temp = arr[rt];

arr[rt] = arr[lt];

arr[lt] = temp;

else 

{

return rt;

}

}

}

static public void quickSort(int[] arr, int lt, int rt) {

int pivot;

if (lt < rt) 

{

pivot = Partition(arr, lt, rt);

if (pivot > 1) 

{

quickSort(arr, lt, pivot - 1);

}  

if (pivot + 1 < rt) 

{

quickSort(arr, pivot + 1, rt);

}

}

}

static void Main(string[] args) {

int[] arr = {57, 2, 85, 46, 75, 1, 90, 13, 50, 9};

int n = 10, i;

Console.WriteLine("Quick Sort");

Console.Write("Initial array is: ");

for (i = 0; i < n; i++) 

{

Console.Write(arr[i] + " ");

}

quickSort(arr, 0, 9);

Console.Write("\nSorted Array is: ");

for (i = 0; i < n; i++) 

{

Console.Write(arr[i] + " ");

}

}

}

}

DSAComplexity-complexity-img4

And that was the implementation of O(n log n) Time Complexity. Next, you will look at the Quadratic Time Complexity.

Quadratic Time Complexity - O(n2)

A method for input size n (where n is very large) executes almost double (n2) the steps, and the algorithm's time changes. The complexity of the method is stated to rise quadratically as the input size increases (n2)

For a particular operation, it takes on the order of N2 steps, where N is the size of the input data.

When the number of steps is proportional to the amount of the input data, we get quadratic complexity.

Code:

using System;

namespace DSAComplexity 

{

class program 

{

public static void Main()

{

for(int i = 0; i < 4; i++)

    for(int j = 1; j < i; j++)          

Console.WriteLine("Subscribe to Simplilearn!!!");

}

}

}

DSAComplexity-complexity-img5

With this, you have a good grip on the technical aspects of Data structures and Algorithm Complexity. 

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Next Steps

The next lesson in your C# training can be "Conditionals in C#." Conditional statements in C# are used when you wish to perform a certain action based on a given condition. Decision-making statements necessitate a few conditions that the program may assess and a set of statements that can be performed if the condition evaluates as true or another statement that can be run if the condition evaluates as false.

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Simplilearn teamed with the Caltech CTME and the Indian Institute of Technology, Kanpur to deliver their Software Development courses. These courses cover the fundamentals of data structures and algorithms in addition to more advanced subjects such as Competitive Programming. As a software engineer, you will learn about data structures such as trees, graphs, and queues.

The comments section below is open for questions and comments about this 'Data Structure and Algorithm Complexity' tutorial. Happy learning!

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