Understanding Data Structures and Algorithms

Posted on Sep 12, 2024 in Computers

Data Structures and Algorithms

Basic Data Structure Operations

FIFO behavior, enqueue, and dequeue are fundamental operations applied to queues.

Types of Data Structures

Trees

A tree is a multilevel data structure representing a hierarchical relationship between a finite set of individual elements called nodes.

Graphs

A graph is a non-linear data structure consisting of a set of nodes (or vertices) and edges (or arcs). Each edge connects two nodes, depicting a relationship between them.

Common Data Structure Operations

Key operations performed on data structures include:

  • Traversing
  • Searching
  • Insertion
  • Deletion
  • Sorting
  • Merging
  • Copying
  • Concatenation

Algorithms

An algorithm is a sequence of steps or instructions to solve a given problem.

Algorithm Analysis

Space Complexity: This analysis focuses on the memory an algorithm needs to solve a problem.

Time Complexity: This analysis measures the efficiency of an algorithm based on its execution time.

Asymptotic Complexity: A measure of time complexity where insignificant terms are disregarded to express the algorithm’s efficiency more concisely.

Algorithm efficiency is measured for average, best, and worst-case scenarios for a given input.

Question-Answers

Q1: List various non-linear data structures. (PTU, May 2004)

Ans: Examples of non-linear data structures include graphs, trees, and multilinked structures like sparse matrices.

Q2: What is the difference between data structures and data types? (PTU, May 2004)

Ans:

Data Structure: A logical or mathematical model for organizing data. Examples: STACK, QUEUE, LINK LIST.

Data Type: Refers to the kind of data a variable can hold in a programming language. Examples: Integer, float, character.

Q3: What are non-linear data structures? Give examples. (PTU, Dec. 2006; May 2007, 2004) OR What do you mean by a non-linear data structure? Give examples. (PTU, May 2011; Dec. 2008)

Ans: Non-linear data structures represent complex relationships beyond physical adjacency. Examples include trees and graphs.