Skip to content

✅ Data Structures: Definition, Classification and Operations

✅ 1. Definition of Data Structure

A Data Structure is a way of organizing, storing, and managing data in computer memory so that it can be used efficiently.

👉 It helps in:

  • Fast access of data
  • Easy insertion and deletion
  • Efficient searching and sorting
  • Better memory utilization

✅ Example:
If we store students’ marks in a simple list, it is easy.
But for faster searching, sorting, or handling huge records, we use proper data structures like arrays, linked lists, trees, etc.

✅ 2. Classification of Data Structures

Data Structures are mainly classified into two types:

✅ (A) Primitive Data Structures

These are the basic data types provided by programming languages.

✅ Examples:

  • int (Integer)
  • float (Real number)
  • char (Character)
  • bool (Boolean)
  • double

📌 These are single-value data types.

✅ (B) Non-Primitive Data Structures

These are complex data structures made using primitive data types.

Non-Primitive Data Structures are further divided into:

✅ 1. Linear Data Structures

In linear data structures, data elements are stored in a sequence (one after another).

✅ Features:

  • Each element has a previous and next element (except first & last)
  • Easy to traverse

✅ Examples:

  1. Array
  2. Linked List
  3. Stack
  4. Queue

📌 Example (Linear order):
10 → 20 → 30 → 40

✅ 2. Non-Linear Data Structures

In non-linear data structures, elements are stored in a hierarchical or network form, not in a sequence.

✅ Features:

  • One element can be connected to many elements
  • Used for complex relationships

✅ Examples:

  1. Tree
  2. Graph

📌 Example (Tree structure):

      A
    /   \
   B     C
  / \
 D   E

✅ 3. Static vs Dynamic Data Structures

✅ Static Data Structures

Static means the size is fixed at compile-time.

✅ Example:

  • Array

📌 Advantage:

  • Easy to access using index

📌 Disadvantage:

  • Fixed size → may waste memory or overflow

✅ Dynamic Data Structures

Dynamic means the size can increase or decrease during runtime.

✅ Examples:

  • Linked List
  • Stack using Linked List
  • Queue using Linked List
  • Trees and Graphs

📌 Advantage:

  • No memory wastage, flexible size

📌 Disadvantage:

  • More complex implementation

✅ 3. Operations of Data Structures

Operations are actions that can be performed on data structures to manage and process data efficiently.

✅ 1. Traversing

Traversal means visiting each element of the data structure one by one.

✅ Example:
Array: 10, 20, 30
Traversal output: 10 20 30

📌 Used for:

  • Displaying elements
  • Processing all data

✅ 2. Insertion

Insertion means adding a new element in the data structure.

✅ Insertion can be:

  • At beginning
  • At end
  • At any position

✅ Example:
Array: 10 20 40
Insert 30 at position 3 → 10 20 30 40

📌 Used in:

  • Adding new student record
  • Adding new node in linked list

✅ 3. Deletion

Deletion means removing an element from the data structure.

✅ Deletion can be:

  • From beginning
  • From end
  • From any position

✅ Example:
Array: 10 20 30 40
Delete 30 → 10 20 40

📌 Used in:

  • Removing invalid record
  • Removing nodes in linked list

✅ 4. Searching

Searching means finding the location of a required element in data structure.

✅ Types of Searching:

  1. Linear Search
  2. Binary Search (sorted data required)

✅ Example:
Array: 10 20 30 40
Search 30 → Found at index 2 / position 3

📌 Used in:

  • Searching a roll number
  • Finding a file record

✅ 5. Sorting

Sorting means arranging elements in ascending or descending order.

✅ Example:
Unsorted: 40 10 30 20
Sorted: 10 20 30 40

✅ Common Sorting Techniques:

  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Merge Sort
  • Quick Sort

📌 Used in:

  • Ranking students
  • Sorting product prices

✅ 6. Merging

Merging means combining two similar data structures into one.

✅ Example:
List 1: 10 20 30
List 2: 40 50
Merged: 10 20 30 40 50

📌 Used in:

  • Combining two arrays or linked lists

✅ 7. Updating

Updating means modifying the value of an existing element.

✅ Example:
Marks list: 10 20 30
Update 20 to 25 → 10 25 30

📌 Used in:

  • Editing student details
  • Updating database records

✅ Conclusion

Data Structures are very important in computer science because they make programs fast, efficient, and manageable.
They are classified into primitive and non-primitive, and further into linear and non-linear, with common operations such as traversing, insertion, deletion, searching, sorting, and merging.