Data Structures Fundamentals
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Questions and Answers

What is the time complexity of accessing an element in an array?

  • O(1) (correct)
  • O(log n)
  • O(n)
  • O(n^2)

    • Which data structure is suitable for implementing a LIFO (Last-In-First-Out) system?

  • Stack (correct)
  • Queue
  • Linked List
  • Array

    • What is the time complexity of the Merge Sort algorithm?

  • O(n)
  • O(log n)
  • O(n^2)
  • O(n log n) (correct)

    • Which searching algorithm is suitable for searching an element in a sorted array?

    <p>Binary Search</p> Signup and view all the answers

    What is the purpose of memoization in Dynamic Programming?

    <p>To avoid redundant computation</p> Signup and view all the answers

    What is the time complexity of Dijkstra's Algorithm?

    <p>O(n log n + m)</p> Signup and view all the answers

    Which data structure is suitable for implementing a hierarchical structure?

    <p>Tree</p> Signup and view all the answers

    What is the time complexity of the Bubble Sort algorithm?

    <p>O(n^2)</p> Signup and view all the answers

    Study Notes

    Data Structures

    Arrays

    • A collection of elements of the same data type stored in contiguous memory locations
    • Each element is identified by an index or key
    • Operations:
      • Access: O(1)
      • Insert: O(n)
      • Delete: O(n)

    Linked Lists

    • A collection of elements where each element points to the next element
    • Each element is a separate object, and memory is allocated dynamically
    • Operations:
      • Access: O(n)
      • Insert: O(1)
      • Delete: O(1)

    Stacks

    • A Last-In-First-Out (LIFO) data structure
    • Operations:
      • Push: O(1)
      • Pop: O(1)
      • Peek: O(1)

    Queues

    • A First-In-First-Out (FIFO) data structure
    • Operations:
      • Enqueue: O(1)
      • Dequeue: O(1)
      • Peek: O(1)

    Trees

    • A hierarchical data structure with nodes having at most two children (left and right)
    • Operations:
      • Insert: O(log n)
      • Delete: O(log n)
      • Search: O(log n)

    Graphs

    • A non-linear data structure consisting of nodes and edges
    • Operations:
      • BFS (Breadth-First Search): O(n + m)
      • DFS (Depth-First Search): O(n + m)

    Algorithms

    Sorting Algorithms

    • Bubble Sort: O(n^2)
    • Selection Sort: O(n^2)
    • Insertion Sort: O(n^2)
    • Merge Sort: O(n log n)
    • Quick Sort: O(n log n)
    • Heap Sort: O(n log n)

    Searching Algorithms

    • Linear Search: O(n)
    • Binary Search: O(log n)

    Graph Algorithms

    • Dijkstra's Algorithm: O(n log n + m)
    • Floyd Warshall Algorithm: O(n^3)
    • Topological Sort: O(n + m)

    Dynamic Programming

    • A method for solving complex problems by breaking them down into smaller subproblems
    • Memoization: storing solutions to subproblems to avoid redundant computation
    • Examples:
      • Fibonacci Series
      • Longest Common Subsequence

    Data Structures

    Arrays

    • Collection of elements of the same data type stored in contiguous memory locations
    • Each element identified by an index or key
    • Operations have time complexities:
      • Access: constant time O(1)
      • Insert: linear time O(n)
      • Delete: linear time O(n)

    Linked Lists

    • Collection of elements where each element points to the next element
    • Each element is a separate object, and memory is allocated dynamically
    • Operations have time complexities:
      • Access: linear time O(n)
      • Insert: constant time O(1)
      • Delete: constant time O(1)

    Stacks

    • Last-In-First-Out (LIFO) data structure
    • Operations have time complexities:
      • Push: constant time O(1)
      • Pop: constant time O(1)
      • Peek: constant time O(1)

    Queues

    • First-In-First-Out (FIFO) data structure
    • Operations have time complexities:
      • Enqueue: constant time O(1)
      • Dequeue: constant time O(1)
      • Peek: constant time O(1)

    Trees

    • Hierarchical data structure with nodes having at most two children (left and right)
    • Operations have time complexities:
      • Insert: logarithmic time O(log n)
      • Delete: logarithmic time O(log n)
      • Search: logarithmic time O(log n)

    Graphs

    • Non-linear data structure consisting of nodes and edges
    • Operations have time complexities:
      • BFS (Breadth-First Search): linear time O(n + m)
      • DFS (Depth-First Search): linear time O(n + m)

    Algorithms

    Sorting Algorithms

    • Bubble Sort: quadratic time complexity O(n^2)
    • Selection Sort: quadratic time complexity O(n^2)
    • Insertion Sort: quadratic time complexity O(n^2)
    • Merge Sort: logarithmic time complexity O(n log n)
    • Quick Sort: logarithmic time complexity O(n log n)
    • Heap Sort: logarithmic time complexity O(n log n)

    Searching Algorithms

    • Linear Search: linear time complexity O(n)
    • Binary Search: logarithmic time complexity O(log n)

    Graph Algorithms

    • Dijkstra's Algorithm: time complexity O(n log n + m)
    • Floyd Warshall Algorithm: cubic time complexity O(n^3)
    • Topological Sort: linear time complexity O(n + m)

    Dynamic Programming

    • Method for solving complex problems by breaking them down into smaller subproblems
    • Memoization: storing solutions to subproblems to avoid redundant computation
    • Examples include:
      • Fibonacci Series
      • Longest Common Subsequence

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    Description

    Learn about the basics of arrays, linked lists, and stacks, including their operations and time complexities.

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