Data Structure Overview

Data Structure Overview

• Definition: A data structure is a way to organize and store data in a computer so it can be accessed and modified efficiently.

Types of Data Structures

1. Primitive Data Structures

   • Basic data types, typically built into programming languages.
   • Examples: integers, floats, booleans, characters.

2. Non-Primitive Data Structures

   • More complex structures built from primitive data types.
   • Divided into two main categories:
     • Linear Data Structures
       • Elements are in a sequential manner.
       • Examples:
         • Arrays: Fixed size, indexed collection of elements.
         • Linked Lists: Collection of nodes with data and a reference to the next node.
         • Stacks: LIFO (Last In, First Out) structure.
         • Queues: FIFO (First In, First Out) structure.
     • Non-Linear Data Structures
       • Elements are not in a sequential manner.
       • Examples:
         • Trees: Hierarchical structure with nodes connected by edges.
           • Binary Tree, Binary Search Tree, AVL Tree, etc.
         • Graphs: Set of nodes and edges describing relationships.
           • Directed, Undirected, Weighted, and Unweighted graphs.

Operations on Data Structures

• Common operations include:
  • Insertion: Adding an element.
  • Deletion: Removing an element.
  • Traversal: Accessing each element (e.g., in-order, pre-order, post-order for trees).
  • Searching: Finding an element (e.g., linear search, binary search).
  • Updating: Modifying existing elements.

Importance of Data Structures

• Efficiency: Affects performance of algorithms.
• Organization: Structures data to facilitate specific operations.
• Memory Management: Helps manage how data is stored and retrieved.

Choosing the Right Data Structure

• Based on:
  • Type of operation needed (searching, inserting, deleting).
  • Data characteristics (size, frequency of access).
  • Time complexity and space complexity of operations.

Commonly Used Data Structures

• Hash Tables: Key-value pairs for quick search and retrieval.
• Heaps: Special tree-based structure satisfying heap property (min-heap/max-heap).
• Sets: Collections of unique elements, allows operations like union, intersection.

Data Structure Definition

• A data structure is a method of organizing and storing data in a computer system, allowing for efficient access and modification.

Types of Data Structures

• Primitive Data Structures: Basic data types generally built into programming languages.
  • Examples: Integers, Floats, Booleans, Characters.
• Non-Primitive Data Structures: More complex structures built using primitive data types.
  • Linear Data Structures: Elements are arranged sequentially.
    • Arrays: Fixed-size collections of elements accessed by index.
    • Linked Lists: Collections of nodes containing data and references to the next node.
    • Stacks: Last-In, First-Out (LIFO) data structure.
    • Queues: First-In, First-Out (FIFO) data structure.
  • Non-Linear Data Structures: Elements are not in a sequential arrangement.
    • Trees: Hierarchical structures composed of nodes connected by edges.
      • Examples: Binary Tree, Binary Search Tree, AVL Tree
    • Graphs: Networks of nodes (vertices) linked by edges, depicting relationships.
      • Types: Directed, Undirected, Weighted, Unweighted.

Operations on Data Structures

• Common operations performed on data structures:
  • Insertion: Adding a new element.
  • Deletion: Removing an element.
  • Traversal: Accessing each element in a specific order.
    • Example: In-order, pre-order, post-order for tree traversal.
  • Searching: Finding a specific element.
    • Example: Linear search, Binary search.
  • Updating: Modifying existing elements.

Importance of Data Structures

• Efficiency: Impact on algorithm performance.
• Organization: Structures data for specific operations.
• Memory Management: Facilitates managing how data is stored and accessed.

Choosing the Right Data Structure

• Factors to consider:
  • Operations needed: Searching, inserting, deleting.
  • Data characteristics: Size, frequency of access.
  • Time and space complexity: Efficiency of operations.

Commonly Used Data Structures

• Hash Tables: Key-value pairs for quick search and retrieval.
• Heaps: Special tree-based structures satisfying the heap property (min-heap/max-heap).
• Sets: Collections of unique elements, supporting operations like union and intersection.