MSBTE Data Structures Using 'C' Video Lectures in FREE | MSBTE Diploma 22317 DSU

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1. Introduction to Data Structures

1.1(a) Concept and need of DS

1.1(b) Abstract Data Type

1.2 Types of Data Structures: (i) Linear Data Structures (ii) Non-Linear Data Structures

1.3 Algorithm Complexity: (i)Time (ii)Space

1.4 Operations on Data Structures: (i)Traversing,(ii)Searching, (iii)Insertion, (iv)Deletion,(v)Sorting

2. Searching and Sorting

2.1 Searching: searching an item in a data set using following methods: (i)Linear Search (ii)Binary Search

2.2 Sorting: sorting of data set in an order using following methods: (i)Bubble Sort, (ii)Selection Sort, (iii)Insertion Sort, (iv)Quick Sort, (v)Radix Sort.

2.2 Sorting: (i)Bubble Sort

2.2 Sorting:(ii)Selection Sort

2.2 Sorting: (iii)Insertion Sort

2.2 Sorting: (iv)Quick Sort

2.2 Sorting: (v)Radix Sort.

3. Stacks and Queues

3.1 Introduction to Stack

3.1(a) -Stack Operations - PUSH, POP

3.1(b) -Stack Operations Conditions - Stack Full / Stack Overflow, Stack Empty / Stack Underflow

3.1(c) -Stack representation in memory using array

3.1(d) -Stack as an ADT

3.1(e) -Applications of Stack

3.2(a) Conversion of infix to postfix expression

3.2(b) Evaluation of postfix expression

3.2(c) Converting an infix into prefix expression

3.2(d) Evaluation of prefix expression

3.2(e) Recursion

3.2(f) Tower of Hanoi

3.3 Introduction to Queue

3.3(a) - Queue Operations - INSERT, DELETE

3.3(b) - Types of Queues :- Linear Queue, Circular Queue, Concept of Priority Queue

3.3(c) - Queue representation in memory using array

3.3(d) - Queue as an ADT

3.3(e) - Queue Operations Conditions - Queue Full, Queue Empty

3.3(f) - Applications of Queue

4. Linked List

4.1 Introduction to Linked List Terminologies: node, Address, Pointer, Information field / Data field, Next pointer, Null Pointer, Empty list.

4.2(a) Type of lists: Linear list

4.2(b) Type of lists: Circular list

4.3(a) Operations on a singly linked list: Traversing a singly linked list, Searching a key in linked list

4.3(b) Inserting a new node in a linked list

4.3(c) Deleting a node from a linked list

5. Trees and Graphs

5.1 Terminologies: tree, degree of a node, degree of a tree, level of a node, leaf node, Depth / Height of a tree, In-degree & Out-Degree, Path, Ancestor & descendant node s

5.2(a) General tree

5.2(b) Binary tree

5.2(c) Binary search tree (BST)

5.2(d) Binary tree traversal: In order trav ersal, Preorder traversal, Post order trav ersal

5.4 Introduction to Graph terminologies: graph, node (Vertices), arcs (edge), directed graph, undirected graph, in-degree, out-degree, adjacent, successor, predecessor, relation path, sink, articulation point.

5.5(a) Adjacency List

5.5(b) Adjacency Matrix of directed / undirected graph.

Syllabus PDF

Data Structures Using 'C' Syllabus PDF

Books / Notes PDF

Data Structures Using 'C' Books / Notes PDF

Important Questions PDF

Data Structures Using 'C' Important Questions PDF

Lab Manual Answers PDF

Data Structures Using 'C' Lab Manual Answers PDF (NA)

External Pratical VIVA / Oral Practice Question PDF

Data Structures Using 'C' External Pratical VIVA / Oral Practice Question PD


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  1. Data Structure using C (H1)
    • What is a Data Structure?
    • Role of C in Data Structures
  2. Types of Data Structures in C (H2)
    • Primitive Data Structures
    • Non-Primitive Data Structures
      • Linear Data Structures
        • Arrays
        • Linked Lists
        • Stacks
        • Queues
      • Non-Linear Data Structures
        • Trees
        • Graphs
  3. Importance of Data Structures (H2)
    • Efficient Data Management
    • Resource Optimization
    • Improved Problem-solving
  4. Choosing the Right Data Structure (H2)
    • Factors to Consider
  5. Common Operations on Data Structures (H2)
    • Insertion
    • Deletion
    • Traversal
    • Searching
    • Sorting

 

Data Structure using C

Whenever we talk about organizing, processing, and storing data, the first thing that often pops into our minds is Data Structures. Just as we organize books on a shelf or files in a cabinet, data needs its own method of arrangement in the digital realm, and that's where data structures come into play. And when it comes to implementing them, the C programming language stands tall as a foundational tool.

 

What is a Data Structure?

In the simplest terms, a data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. Think of it as the 'containers' where data resides.

 

Role of C in Data Structures

The C language, known for its performance and close-to-hardware capabilities, has been a popular choice for implementing data structures. Its simplicity and procedural nature provide programmers with a robust platform to explore and manipulate data structures at a granular level.

 

Types of Data Structures in C

Categorizing and understanding these containers help in selecting the right one for the job.

 

Primitive Data Structures

These are the basic data structures that are supported at the machine level. Examples include int, char, float, and pointers.

 

Non-Primitive Data Structures

Let's delve deeper into the containers:

 

Linear Data Structures

  • Arrays: Contiguous memory locations used to store elements of the same data type.
  • Linked Lists: Elements are stored in nodes, and each node points to the next node.
  • Stacks: A last-in-first-out (LIFO) structure. Think of it as a stack of plates.
  • Queues: A first-in-first-out (FIFO) structure, resembling a queue in real life.

 

Non-Linear Data Structures

  • Trees: Hierarchical data structures with a root and subtrees of children.
  • Graphs: Sets of nodes connected by edges, suitable for representing network structures.

 

Importance of Data Structures

"Why should I even bother?" you might ask. Here’s why:

  • ·        Efficient Data Management: A well-chosen data structure can make a big difference in how data is managed, making operations like retrieval faster.
  • ·        Resource Optimization: With the right structure, you can optimize memory usage and processing time.
  • ·        Improved Problem-solving: Many algorithmic problems become easier to solve when paired with an appropriate data structure.
  • ·        Choosing the Right Data Structure: The right tool for the right job makes tasks easier:

 

Factors to Consider

  • Nature of Operations: Some structures are better suited for retrieval, while others excel at insertion or deletion.
  • Volume of Data: Large datasets might benefit from structures that optimize memory usage.
  • Access Patterns: Sequential access might favor lists, while random access suits arrays.

 

Common Operations on Data Structures

Let's not forget the various operations we perform on these structures:

  • ·        Insertion: Placing a new item in the structure.
  • ·        Deletion: Removing an item from the structure.
  • ·        Traversal: Visiting and examining each element.
  • ·        Searching: Finding a specific item.
  • ·        Sorting:  Arranging elements in a particular order.

 

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