Data Structures Notes: All UNITS

UNIT – 1

1. Introduction to Data Structures

• Definition and Importance: Understanding the role of data structures in problem-solving and efficient algorithm design.
• Classification: Primitive and non-primitive data structures, linear and non-linear data structures.

UNIT – 2

2. Arrays

• Introduction to Arrays: Definition, types (one-dimensional, two-dimensional, multi-dimensional).
• Array Operations: Insertion, deletion, traversal, searching, and sorting.
• Applications: Practical applications of arrays in various scenarios.

UNIT – 3

3. Linked Lists

• Introduction to Linked Lists: Definition, advantages over arrays, types (singly, doubly, circular linked lists).
• Operations on Linked Lists: Insertion, deletion, traversal, searching, and reversing.
• Applications: Implementing stacks and queues using linked lists.

UNIT – 4

4. Stacks

• Definition and Concepts: LIFO (Last In, First Out) principle, stack operations (push, pop, peek).
• Implementation: Using arrays and linked lists.
• Applications: Function calls, expression evaluation (infix, postfix, prefix), recursion.

UNIT – 5

5. Queues

• Definition and Concepts: FIFO (First In, First Out) principle, queue operations (enqueue, dequeue, front, rear).
• Types of Queues: Simple queue, circular queue, priority queue, double-ended queue (deque).
• Implementation: Using arrays and linked lists.
• Applications: Job scheduling, simulation, breadth-first search (BFS).

UNIT – 6

6. Trees

• Introduction to Trees: Basic terminology (root, leaf, child, parent, sibling), types of trees (binary tree, binary search tree, AVL tree, etc.).
• Binary Trees: Representation, traversal methods (in-order, pre-order, post-order).
• Binary Search Trees (BST): Properties, operations (insertion, deletion, searching).
• Balanced Trees: AVL trees, rotations for balancing.

UNIT – 7

7. Graphs

• Introduction to Graphs: Basic terminology (vertices, edges, degree, path, cycle), types of graphs (directed, undirected, weighted, unweighted).
• Graph Representations: Adjacency matrix, adjacency list.
• Graph Traversals: Depth-first search (DFS), breadth-first search (BFS).
• Applications: Shortest path algorithms (Dijkstra’s, Floyd-Warshall), minimum spanning tree (Kruskal’s, Prim’s).

UNIT – 8

8. Hashing

• Introduction to Hashing: Hash functions, properties of good hash functions.
• Collision Resolution Techniques: Open addressing, chaining.
• Applications: Implementing dictionaries, databases.

UNIT – 9

9. Sorting and Searching Algorithms

• Sorting Algorithms: Bubble sort, selection sort, insertion sort, merge sort, quick sort, heap sort.
• Searching Algorithms: Linear search, binary search.
• Algorithm Analysis: Time complexity and space complexity.

UNIT – 10

10. File Handling

• Basic Concepts: Types of files (text, binary), file operations (open, close, read, write).
• File Handling in C: Using file pointers, seek, tell, read, write functions.
• Applications: Reading and writing data structures to files.

Recommended Books and Resources

• “Data Structures Using C” by Reema Thareja: Comprehensive guide to data structures in C.
• “Data Structures and Algorithm Analysis in C” by Mark Allen Weiss: In-depth coverage of data structures and algorithms.
• “The C Programming Language” by Brian W. Kernighan and Dennis M. Ritchie: Classic book for mastering C programming.
• Online Resources: TutorialsPoint, GeeksforGeeks, Coursera, edX.

Practical Assignments

• Array Manipulations: Implementing and testing various operations on arrays.
• Linked List Operations: Creating linked lists and performing insertion, deletion, and traversal operations.
• Stack and Queue Applications: Implementing stacks and queues and using them in practical applications.
• Tree and Graph Algorithms: Implementing and testing various tree and graph algorithms.
• Sorting and Searching: Writing and analyzing various sorting and searching algorithms.
• File Handling Projects: Implementing file operations to read and write data structures to files.

Practical Skills

• Algorithm Design and Analysis: Developing efficient algorithms for various data structures.
• Programming Proficiency: Writing clean, efficient, and well-documented code in C.
• Problem-Solving: Applying data structure concepts to solve real-world problems.
• Debugging and Testing: Techniques for debugging and testing data structure implementations.