Day 1: Introduction to Algorithmic Thinking and Recursion

Session 1: Fundamentals of Algorithmic Thinking

• What is an algorithm?
• Understanding time and space complexity (Big O notation)
• Common algorithmic paradigms and their applications

Session 2: Recursion and Backtracking

• Understanding recursive problem-solving
• Implementing recursive algorithms
• Introduction to backtracking and its applications

Session 3: Hands-on Lab – Solving Problems with Recursion

• Implementing recursive functions in programming exercises
• Debugging and optimizing recursive solutions

Day 2: Divide and Conquer & Dynamic Programming

Session 1: Divide and Conquer Strategy

• Concept of divide and conquer
• Implementing merge sort and quicksort algorithms
• Applying divide and conquer to real-world problems

Session 2: Introduction to Dynamic Programming

• Understanding overlapping subproblems and optimal substructure
• Bottom-up vs. top-down approach in dynamic programming
• Common dynamic programming problems and solutions

Session 3: Hands-on Lab – Implementing Divide and Conquer & Dynamic Programming

• Solving real-world problems using divide and conquer
• Implementing dynamic programming solutions in code

Day 3: Greedy Algorithms and Optimization Techniques

Session 1: Introduction to Greedy Algorithms

• Understanding greedy choice property and optimal substructure
• Common greedy algorithms (Huffman coding, activity selection)
• When to use greedy algorithms vs. dynamic programming

Session 2: Real-World Problem Solving with Algorithmic Thinking

• Applying algorithmic techniques to optimization problems
• Strategy for selecting the right algorithm for a problem
• Trade-offs between different approaches

Session 3: Hands-on Lab – Solving Problems with Greedy Algorithms

• Implementing and testing greedy algorithms
• Comparing greedy vs. other approaches for problem-solving

Session 4: Code Review, Best Practices, and Final Q&A

• Reviewing problem solutions for efficiency and scalability
• Best practices for writing and optimizing algorithms
• Course wrap-up and next steps in algorithmic learning