Data Structure and Algorithm Course Syllabus

12-07-2024

About The Course:

Our Data Structure and Algorithm course is designed to equip you with the essential skills needed to optimize your coding and problem-solving abilities. This comprehensive program is meticulously crafted to provide participants with a deep understanding of fundamental concepts that underpin efficient and effective problem solving in software development.This comprehensive program is meticulously crafted to provide participants with a deep understanding of fundamental concepts that underpin efficient and effective problem-solving in software development.

Course Certification:

we understand the importance of recognizing your achievements and the dedication you invest in your learning journey. Upon successful completion of our course, each participant will be awarded an official certificate, acknowledging the mastery of skills and knowledge gained during the program. These official certifications, bearing the seal, are widely recognized in the industry and demonstrate your proficiency in. We take pride in celebrating your accomplishments and providing tangible evidence of the expertise you've acquired.

1. Data Representation

• Basic Concepts of Data Structures:
• Overview of data structures and their importance in programming.
• Abstract Data Types (ADTs):
• Understanding the concept of ADTs and their role in designing algorithms.
• Elementary Data Types:
• Exploring fundamental data types such as integers, floats, characters, and booleans.
• Mathematical Preliminaries:
• Asymptotic Notations: Understanding Big O, Theta, and Omega notations.
• Efficiency of Algorithms: Evaluating algorithm efficiency in terms of time and space.
• Notions of Time and Space Complexity: Analyzing algorithm performance in different
dimensions.
• Notions of Performance Analysis:
• est, Worst, and Average Case Analysis: Exploring performance scenarios.
• Solving Simple Recurrences: Techniques for solving recurrence relations.
• Divide and Conquer Type Recurrences: Strategies for solving recurrence relations in
divide and conquer algorithms.

2. Computations on Arrays

• Binary Search:
• Algorithm for binary search.
• Best and worst-case performance analysis.
• Sorting Algorithms:
• Bubble Sort, Insertion Sort, and Quicksort.
• Best and worst-case performance analysis.
• External Merge Sort:
• Overview of external merge sort for large datasets.
• Heaps and Heapsort:
• Understanding heaps and the heapsort algorithm.
• Priority Queues Using Heaps: Applications of heaps as priority queues.

3. Linked Lists

• Single and Doubly Linked Lists:
• Implementation and manipulation of single and doubly linked lists.

4. Queue and Stack Data Structures

• Array-Based and Linked List-Based Implementations:
• Design and implementation of queues and stacks using arrays and linked lists.
• Infix to Postfix Conversion and Expression Evaluation:
• Algorithms for converting infix expressions to postfix and evaluating expressions.

5. Graphs

• Adjacency Matrix and Adjacency List Representations:
• Methods for representing graphs in computer memory.
• DFS and BFS (Depth-First Search and Breadth-First Search):
• Graph traversal algorithms.

6. Hash Tables

• Hash Functions:
• Techniques for creating effective hash functions.
• Open Addressing and Chaining:
• Methods for handling collisions in hash tables.

7. Trees

• Rooted Tree Representation:
• Understanding the structure of rooted trees.
• Traversal of Trees:
• Different traversal techniques such as inorder, preorder, and postorder.
• Binary Trees:
• Concepts and operations related to binary trees.

8. Search Tree

• Binary Search Trees:
• Characteristics and operations associated with binary search trees.

9. Balanced Search Trees

• 2-3-4 Tree:
• Structure and operations on 2-3-4 trees.
• B-Tree:
• Overview of B-Trees as external memory data structures

Data Structure and Algorithm Course Syllabus

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