Session 1: Introduction to Advanced Programming Techniques

• Welcome and Overview
• Introduction to the workshop objectives and schedule
• Brief discussion on the importance of advanced programming techniques
• Structured Programming
• Principles of structured programming
• Control structures: sequence, selection, and iteration
• Best practices for writing clean and maintainable code
• Recursion
• Introduction to recursion and its applications
• Comparison between iterative and recursive solutions
• Example problems: factorial, Fibonacci sequence

Session 2: Data Types and Structures

• Advanced Data Types
• Review of primitive data types
• Introduction to composite data types: arrays, records, tuples
• Operations and applications of each data type
• Abstract Data Types (ADTs)
• Concept of ADTs and their significance
• Implementation and use cases of stacks, queues, linked lists

Session 3: Object-Oriented Programming (OOP)

• Fundamentals of OOP
• Key concepts: classes, objects, inheritance, polymorphism, encapsulation
• Benefits of OOP in software development
• Practical Exercise
• Hands-on activity: Create a simple class in Python
• Implement inheritance and polymorphism through a small project

Session 4: Modular Programming and Code Reusability

• Modular Programming
• Importance of modularity in programming
• Techniques for decomposing a problem into modules
• Writing and using functions and procedures
• Code Reusability
• Best practices for writing reusable code
• Creating and using libraries and modules
• Example: practical programming activity in Python

Session 5: Error Handling and Debugging

• Error Handling
• Types of errors: syntax, runtime, logical
• Techniques for error detection and correction
• Using try-except blocks in Python
• Debugging Techniques
• Common debugging strategies and tools
• Practical tips for identifying and fixing bugs
• Hands-on debugging session with provided faulty code

Session 6: Advanced Algorithms and Wrap-Up

• Advanced Algorithms
• Overview of searching and sorting algorithms (e.g. quicksort, mergesort)
• Introduction to algorithm complexity (Big O notation)
• Wrap-Up and Q&A
• Review key concepts covered
• Address any questions or difficulties
• Additional resources for further learning
• Final practical exercise: Solve a complex problem using the techniques learned