Before You Learn Programming, Understand Computational Thinking
Many people aspire to become a programmer as the demand for professionals in the IT industry is increasing nowadays. Unfortunately, many fail to become one because they skip the fundamental mindset of programming: computational thinking. It is defined as the act of working through a problem in a methodical and logical manner, similar to how a computer goes through its programs in a logical order.?
How Important is Computational Thinking?
Coding is the process of programming digital tools with algorithms, whereas computational thinking is the problem-solving process that can support coding. It is a way to implement ideas that go through computational thinking steps. In this case, algorithms are a set of logic-based processes that communicate with the computer system and help them in carrying out various tasks.?
Before finally entering the realm of coding, aspiring developers must be familiar with current algorithms such as scheduling, route-finding, and compression techniques, but they must also be able to build new algorithms. Computational thinking ensures that the process can be repeated by developing an algorithm. In other words, it is as much about the problem-solving process as it is about addressing the problem.
Computational thinking involves far more than simply following instructions; it is more of a whole set of abilities that helps you in thinking through a problem and understanding the process so that it will be simpler the next time. Long before you can learn how to code and which code you need to create either a website or an app, you need to get your framework of thinking with four principles of computational thinking:?
- Decomposition
- Pattern recognition
- Abstraction
- Algorithm design
To understand these four principles better and how they work, here is a closer look at each of the computational thinking skills.
Decomposition
Decomposition is the process of breaking down a large problem into smaller, less difficult pieces. These issues should be addressed one at a time until the major issue is resolved. This stage also allows you to have a better understanding of the situation at hand by properly identifying all of its components. Decomposition is important because dealing with many individual processes independently rather than resolving one huge issue allows for faster and more visible solutions, higher efficiency, and motivates you to look at a problem in more depth.
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Pattern Recognition
After decomposing the problem into smaller, less difficult sections, the next step is to examine what similarities the problems have. This is referred to as pattern recognition. Since each particular problem shares similarities, you will be able to construct a solution that is relevant to a wide range of difficulties. Pattern recognition will help you solve problems more effectively since they will be able to use what they have already created as a solution to a new comparable challenge. As you build more programs, you will accumulate a personal library of solutions to common problems. This library may be used to identify patterns and generalize.?
Abstraction
Abstraction refers to focusing just on the most essential and crucial information while dismissing any aspects that would impede progress. Ignoring irrelevant features helps you get to the desired outcome. The general characteristics that are similar to each element inside particular details are focused on in abstraction. After collecting the general characteristics, a model of the problem may be developed, which can later be used to create an algorithm. Abstraction is important because it allows you to construct a basic understanding of what the problem is and how to solve it, allowing them to form an insight into the problem, which is known as the model.
Algorithm Design
An algorithm is a strategy that contains a set of subsequent actions to solve a problem, effectively running a program. This involves developing a roadmap or a workflow, in other words, a step-by-step approach to problem-solving. Conditional logic is an important element in this context. This means that a certain action or set of steps may only be done if certain circumstances are met. A good algorithm is always accurate and can only have one interpretation. It contains the necessary information that leads to the flow's completion. It is not overloaded by unnecessary details. The algorithm's objective must be realistic, addressing all feasible and reasonable situations.?
Computational thinking is an ongoing exercise, not a one-time activity. You should practice it many times until you feel comfortable doing it instinctively. This process is commonly referred to as Prodigy. People with Prodigy capability will often do pre-coding logic checks without the need to go back and forth in jotting down ideas on paper. Understanding how computational thinking works while putting it into practice is essentially the very first step before you proceed to learn a programming language. Good luck!
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Lead Java Developer at Platinum Analytics & Technologies
2 å¹´Interesting way to look at solving problems, however that is where alot of problems come from, overthinking a simple solution for the sake of building something. Instead aspiring developers need to understand that coding is not for everyone and should consider other roles that fit their skills such as ui/ux or business analyst and the like. The realm of IT has expanded to more than coding. Just a thought ??