Introduction to Data Structures

Study Notes

Introduction to Data Structures

Data is a collection of raw facts and figures.
Data structures are techniques for organizing data logically and mathematically.
Data structures organize data in a specific format.
Linked Lists are a common example of data structures, where data is stored in nodes linked together by addresses.

Purpose of Data Structures

Data structures improve program efficiency by reducing storage requirements.
They help manage memory effectively, reducing memory waste and increasing program speed.

Classification of Data Structures

Linear vs Non-Linear:
- Linear data structures store data in a sequential manner, such as linked lists.
- Non-linear structures store data in a hierarchical or network form, like trees and graphs.
Homogeneous vs Non-Homogeneous:
- Homogeneous structures store similar types of data, like arrays.
- Non-homogeneous structures store different types of data, like structures and unions.
Static vs Dynamic:
- Static data structures have a fixed size allocated at compile time.
- Dynamic data structures can expand or shrink in size during program execution, based on the needs of the program.

Operations Performed on Data Structures

Searching: Finding a specific element in a data structure.
Traversing: Accessing and processing each element in a data structure sequentially.
Inserting: Adding a new element to a data structure.
Updating: Modifying an existing element in a data structure.
Deleting: Removing an element from a data structure.
Merging: Combining two data structures into one.
Sorting: Arranging elements in a specific order, like ascending or descending order.

What is an Algorithm?

An algorithm is a step-by-step description of a program written in a general language.
Algorithms provide a clear set of instructions for solving a problem.

Properties of Algorithms

Input: Every algorithm requires input data.
Output: Every algorithm produces an output after processing input data.
Definiteness: Every instruction within an algorithm must be clear and unambiguous.
Finiteness: An algorithm must terminate after a finite number of steps.
Effectiveness: Each instruction in an algorithm must be basic enough to be carried out in a finite amount of time.

Example: Adding Two Numbers

Algorithm:
- Begin
- Input: Two numbers, A and B.
- Sum: A + B
- Output: Display the sum of A and B.
- End

C++ Program:

#include <iostream>
using namespace std;

int main() {
    int A, B, sum;
    cout << "Enter two numbers: ";
    cin >> A >> B;
    sum = A + B;
    cout << "Sum of A and B: " << sum << endl;
    return 0;
}

Example: Printing Natural Numbers

Algorithm:
- Begin
- Set: I = 1
- Repeat (until I <= 10)
  - Print: Value of I
  - Increment: I = I + 1
- End

C++ Program:

#include <iostream>
using namespace std;

int main() {
    int I = 1;
    while (I <= 10) {
        cout << I << " ";
        I++;
    }
    return 0;
}

Description

Explore the fundamentals of data structures in this quiz, focusing on their classification, purpose, and efficiency in programming. Understand the differences between linear and non-linear structures, as well as homogeneous and non-homogeneous types. Test your knowledge on how data structures can optimize memory usage and program speed.