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Comprehensive Data Structures & Algorithms Notes for Computer Science Students, Study notes of Computer science

Computer science

Description: This document contains comprehensive and original notes on Data Structures & Algorithms designed for computer science students. It is ideal for undergraduate courses, exam preparation, and self-study. The notes are written clearly with step-by-step explanations, practical Python code examples, tables, and practice questions to reinforce learning. Content Overview / Index: Introduction – What are data structures and algorithms, and why they are important. Types of Data Structures Linear (Arrays, Linked Lists, Stack, Queue) Non-linear (Trees, Graphs) Algorithms Basics – Time and space complexity, Big O notation. Sorting Algorithms – Bubble Sort, Insertion Sort, Merge Sort, Quick Sort with Python examples. Searching Algorithms – Linear Search, Binary Search with implementations. Key Data Structures with Examples – Stack, Queue, Binary Search Tree (BST).

Typology: Study notes

2025/2026

Available from 04/03/2026

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Data Structures & Algorithms – Complete Study Notes
1. Introduction
Data Structures: Organized ways to store and manage data for efficient access
and modification.
Algorithm: A step-by-step procedure to solve a problem.
Importance:
Optimizes memory usage
Reduces computation time
Forms the foundation of software development
2. Types of Data Structures
2.1 Linear Data Structures
Elements stored sequentially
Examples:
1. Array – Fixed-size collection of elements
arr = [1, 2, 3, 4]
print(arr[2]) # Output: 3
2. Linked List – Nodes connected via pointers
3. Stack – Last In First Out (LIFO)
4. Queue – First In First Out (FIFO)
2.2 Non-linear Data Structures
Elements not in sequence
Examples:
1. Tree – Hierarchical structure
Binary Tree, Binary Search Tree (BST)
2. Graph – Nodes connected by edges
Directed / Undirected
Weighted / Unweighted
3. Algorithms Basics
Definition: A set of rules or steps to solve a problem.
Complexity:
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Data Structures & Algorithms – Complete Study Notes

1. Introduction Data Structures : Organized ways to store and manage data for efficient access and modification. Algorithm : A step-by-step procedure to solve a problem. Importance :  Optimizes memory usage  Reduces computation time  Forms the foundation of software development 2. Types of Data Structures 2.1 Linear Data Structures  Elements stored sequentially  Examples: 1. Array – Fixed-size collection of elements arr = [1, 2, 3, 4] print(arr[2]) # Output: 3 2. Linked List – Nodes connected via pointers 3. Stack – Last In First Out (LIFO) 4. Queue – First In First Out (FIFO) 2.2 Non-linear Data Structures  Elements not in sequence  Examples: 1. Tree – Hierarchical structure  Binary Tree, Binary Search Tree (BST) 2. Graph – Nodes connected by edges  Directed / Undirected  Weighted / Unweighted 3. Algorithms Basics Definition: A set of rules or steps to solve a problem. Complexity:

Time Complexity – How fast an algorithm runs  Space Complexity – How much memory it uses Big O Notation: Complex ity Example O(1) Accessing array element O(n) Linear search O(log n) Binary search O(n^2) Bubble sort

4. Sorting Algorithms Algorithm Time Complexity Best Use Case Bubble Sort O(n^2) Small arrays Insertion Sort O(n^2) Almost sorted data Merge Sort O(n log n) Large datasets Quick Sort O(n log n) General- purpose Python Example – Bubble Sort def bubble_sort(arr): n = len(arr) for i in range(n): for j in range(0, n-i-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr print(bubble_sort([5, 2, 9, 1])) # Output: [1, 2, 5, 9] 5. Searching Algorithms 1. Linear Search def linear_search(arr, x): for i in range(len(arr)): if arr[i] == x:

  1. Implement a stack using a linked list.
  2. Sort an array using merge sort.
  3. Find the height of a binary tree.
  4. Write a program for breadth-first traversal of a graph. 8. Tips for Revision  Use diagrams for trees and graphs.  Write small code snippets by hand.  Solve past questions using multiple data structures. This is original content , ready for Docsity. You can enhance it by:  Adding diagrams of stacks, queues, trees, and graphs  Including color-coded tables for complexity  Adding extra exercises