Introductory guide to Computational Thinking

Introductory guide to Computational Thinking

What is Computational Thinking?

Model of computation is a terminology for mathematical abstraction that formulates computational thinking. Computation relies on these models.

Computational thinking involves applying appropriate methods of thinking in order to formulate problems whose solutions can be expressed as a sequence of computable steps or an algorithm. Essentially, it enables us to break down big problems into small bits to understand them and look for possible answers. Such solutions may then be presented in such a manner as the computer system may understand them, or as humans may understand them, or both.

4 Key Techniques of Computational Thinking:

  1. Decomposition

    Decomposition is the initial stage of computational thinking. While this may be called different names based on the school of thought, the basic process is the same: when solving a complex problem, you have to split it down into minor, easily understood segments (TECHORG, 2023).

    Decomposition is one of the essential concepts in computational thinking which breaks down a problem into many smaller ones and makes the whole problem manageable. Moreover, it enables problem solvers to clearly define or interpret the problem at hand and to simplify the problem through pattern recognition and abstraction (, 2023).

  2. Pattern Recognition

    Recognizing patterns is another part of computational thinking. It is through this that one can determine the relationships or patterns between various components of a bigger problem. Pattern recognition will provide more simplification of the problem and assist in the creation of a deeper insight into the problem itself (, 2023).

  3. Abstraction

    Abstraction involves distilling the most crucial details from each decomposed challenge. It defines or generalizes, what should be done to address the problem as a whole. The third stage of computational thinking assists students to recognize how they can apply these relevant characteristics to solve another area within the same problem (, 2023).

  4. Algorithmic Thinking

    The last part of computational thinking includes algorithmic thinking. This is the procedure of determining a sequential solution to the problem which can be repeated to produce a guaranteed and consistent outcome. This solution will be a progressive process in which the computer will solve for a modern definition of computation thinking as a computer science concept. This process may also be done either partially or in its entirety by human beings (TECHORG, 2023).

Relevance to My Career as a Mortgage Officer and Future Learning:

In the dynamic environment of mortgage and finance, numerous complex issues which require structured and quick methods of solution are usually given to me to tackle. It is, in fact, here that my understanding of the Computational Thinking comes into its full manifestation.

Data Analysis and Decision Making: As part of my work, I deal with huge numbers of information starting from client’s history and their financial background to market changes. With Computational Thinking, I know how to break down this data into smaller bits and analyze it in depth. Through identifying patterns in financial behaviors or the market trends, I could be able to give a proper decision on interest rates or loans approval.

Streamlining Processes: The way Computational Thinking assists me narrows down on important details while disregarding the unnecessary ones is through the principle of abstraction. The end result of this is that my workflow is more streamlined and my clients get prompt and accurate services.

Risk Assessment: One significant aspect of Computational Thinking is algorithms, which help me assess the risks involved in lending to specific persons and investing in particular properties. A systematic approach reduces the risk of human errors and biases resulting in more sustainable financial decisions.

Benefits for My Future Learning:

Adaptability in a Tech-Driven Landscape: With time, the mortgage industry is becoming more technology-friendly, and my background in computational thinking prepares me well for this change. This flexibility is important for me to keep growing and remain relevant in the industry.

Enhanced Problem-Solving Process: My ability to face new challenges is improved by sharpening my computational thinking skills that make me better at solving problems. In the changing mortgage landscape, I have the skills to go through different systems in an orderly manner.

Interdisciplinary Collaboration: There are numerous principles involved in computational thinking and not only that, but these can also be applied to other fields apart from Computer Science. They resonate across various domains. As part of my job, I frequently work with specialists in various fields, including those from real estate, legal and financial fields. Having a clear grasp of Computational Thinking enables better collaboration and communication, thus elevating my interdisciplinary practices.

To me, Computational thinking is not just a utility, but a state of mind built on efficiency, accuracy, and permanent education. It is an indispensable asset for me as a Mortgage Officer putting me at the forefront of industry innovations, equipping me with the tools necessary to handle the challenges of the future.

References (2023). TECH TIP: Computational Thinking.