Computational Thinking: Thinking Abstractly

Abstraction is a technique in computer science used to reduce and simplify complex systems. This reduction helps to eliminate unnecessary details and highlight the essential features needed to understand a problem.
The process of thinking abstractly involves ignoring the insignificant details of a particular system or problem and focusing on the information that is crucial for problem-solving.
In programming, abstraction can be applied at various levels such as high-level (algorithm abstraction) to low level (data abstraction).
Algorithm abstraction refers to the simplification of a complex algorithm into an easy-to-understand and easy-to-manipulate format. For example, when using a sorting algorithm, you only need to understand that it sorts a list in a particular order, not how it does the sorting.
Data abstraction allows programmers to handle complex data structures with ease. Using this method, complex structures are simplified into basic operations and functions.
Abstraction promotes efficiency in software development as it facilitates modular programming. This allows different modules of a programme to be developed and tested separately, as each module should function correctly regardless of the inner workings of the other modules.
Understanding abstraction is crucial to efficiently developing software applications as it can help break down complicated tasks into manageable parts.
Object-oriented programming utilises the concept of data abstraction through classes. A class acts as a blueprint for creating objects (a specific instance of a class), encapsulating certain data and methods related to them.
Benefits of abstraction include code reusability, improved readability, reduced complexity, and increased security of data.
However, there can also be drawbacks, such as the risk of oversimplification when abstracting a system or problem which could result in missing essential details.