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Advanced programming transcends basic programming skills by emphasizing sophisticated constructs, paradigms, and techniques necessary for modern application development. Key areas include object-oriented programming, data abstraction, inheritance, polymorphism, exception handling, dynamic memory management, file handling, multithreading, functional programming, design patterns, and advanced data structures. Understanding these concepts is essential for building efficient, scalable, and maintainable software systems.
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References
Chapter_1_Overvi.pdfClass Notes
Memorization
What we have learnt
Final Test
Revision Tests
Term: Procedural Programming
Definition: A programming paradigm that uses procedures or routines to structure the program.
Term: ObjectOriented Programming (OOP)
Definition: A programming paradigm organized around objects and classes, promoting encapsulation, inheritance, and polymorphism.
Term: Data Abstraction
Definition: The concept of hiding complex implementation details while exposing only the necessary parts.
Term: Inheritance
Definition: A mechanism that allows a class to acquire properties and behaviors from another class.
Term: Polymorphism
Definition: The ability of different classes to be treated as instances of the same class through common interfaces.
Term: Exception Handling
Definition: A construct designed to handle runtime errors and manage exceptions in programming.
Term: Dynamic Memory Management
Definition: The process of allocating memory at runtime using constructs like pointers and references.
Term: Multithreading
Definition: A concurrent execution model that allows multiple threads to run simultaneously, improving resource utilization.
Term: Functional Programming
Definition: A programming paradigm that treats computation as the evaluation of mathematical functions and focuses on immutable data.
Term: Design Patterns
Definition: Reusable solutions to common design problems that promote good software design practices.
Term: Advanced Data Structures
Definition: Data organization techniques such as trees, graphs, and hash tables that help optimize performance and problem-solving.