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This comprehensive course on Data Structures and Algorithms (DSA) is designed to provide students with an in-depth understanding of essential data structures and algorithms. Through a combination of theoretical concepts and practical applications, students will learn how to efficiently store, organize, and manipulate data. This course is suitable for computer science students, software developers, and anyone interested in mastering DSA.

Learning Objectives:

By the end of this course, students will be able to:

  1. Understand the fundamental concepts of data structures and algorithms.
  2. Analyze the efficiency and performance of different data structures and algorithms.
  3. Implement various data structures and algorithms in a programming language.
  4. Solve complex computational problems using appropriate data structures and algorithms.
  5. Develop problem-solving and critical-thinking skills essential for software development and computer science.

Prerequisites: Basic programming skills

Materials Needed:

  • Computer with internet access.
  • Headphones.

Key Outcome:

By the end of this course, students will have a strong foundation in data structures and algorithms, enabling them to tackle complex computational problems and excel in software development and computer science careers.

Assessment:
Assessment will be based on completion of projects, participation in class activities, and a final project showcase.

Certification: Certificate of completion provided upon finishing the course requirements.

Course Duration: 32 credit hours – 4 months

Days & Timings: One-Hour Interactive Class, Twice a Week (Flexible Timings)

Course Format: Normal Track – 2 hours per week, Fast Track – 4 hours per week

Eligible Age Group: 14 and above

Fee: Monthly – USD 100 | Full Course – USD 400

Instructor:

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Course Content

Introduction to Data Structures and Algorithms
Definition and importance of data structures and algorithms
Complexity analysis: Big O notation, time and space complexity
Arrays and Linked Lists
Arrays: Definition, operations, advantages, and limitations
Stacks and Queues
Trees
AVL Trees: Definition, rotations (single, double), balancing AVL trees
Heaps: Min-heap, max-heap, heap operations, heap sort
Graphs
Graphs: Definition, types (directed, undirected, weighted, unweighted)
Graph representation: Adjacency matrix, adjacency list
Graph traversal: Depth-First Search (DFS), Breadth-First Search (BFS)
Hashing
Applications of hashing
Sorting Algorithms
Bubble sort, selection sort, insertion sort
Merge sort, quick sort, heap sort
Comparison of sorting algorithms: time and space complexity
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