Since data science is about addressing actual issues, it makes sense that some skills are useful assets in their constantly-evolving toolset.
Any prospective data scientist should focus on computational thinking as part of their education, since it teaches fundamental computer science ideas and how to approach complicated issues through abstraction and deconstruction.
Computational thinking is a crucial ability in the age of digital-first technology, not just for aspiring data scientists, but for everybody who wants to take part in the computational world.
In order to be ready for the evolution of the labor market and the future of work, which will be shaped by pervasive automation, artificial intelligence, and machine learning, it is imperative to emphasize computational thinking abilities as a key component of education and professional development.
In this article, we’ll look into computational thinking in detail, covering its elements, value, and a lot more.
So, what is Computational thinking?
Computational thinking, also known as algorithmic thinking, is a methodical technique to address a complex problem by breaking it down into smaller, easier processes that can be done by a computer or machine.
It is crucial to solving an issue in such a way that a computer can perform the process since it signifies that the answer can be applied to similar problems in other contexts.
Computational thinking includes adopting an agile, innovative, and flexible attitude in order to work through challenges and potential solutions as efficiently as possible, as well as successfully using and analyzing data.
The term “computational thinking” comes from the way computer scientists think, but it is now recognized as a way of thinking that anybody can apply to solve issues in their personal or professional lives.
Therefore, the goal is not to use thinking that resembles that of a machine, but rather to create problem-solving strategies that computer scientists commonly use.
Computational thinking is a crucial tool for data scientists since it can be used to tackle a wide range of quantitative and data-intensive challenges.
This method can be used to solve problems in a variety of fields, including mathematics and artificial intelligence. This approach also makes use of the Python programming language, which is utilized to represent the answer on a computer during the statistical analysis step.
Why is Computational Thinking essential?
These methods of problem-solving can be applied to a range of topics using computational thinking. Additionally, there are abilities that computational thinking shares with those utilized in other STEM fields as well as the arts, social sciences, and humanities.
Utilizing the power of computers outside of the screen and keyboard is encouraged by computational thinking. Additionally, it could help us improve equity in computer science education.
We can encourage computer science’s integration with other subject areas and introduce more students to the potential of computer science by putting a focus on the problem-solving abilities that are at its core.
Additionally, computational thinking makes it possible for us to investigate the potential and constraints of technology while it is being produced.
We can assess who is developing technology and why, and we can consider critically how it can affect society.
Core Components of Computational Thinking
Decomposition is the foundational element of computational thinking. In order to make the problem easier to solve, this stage entails breaking it down into smaller components.
An issue is easier to fix the more you can dissect it. The parts of a bike can be disassembled as a useful decomposition practice. A bicycle’s frame, wheels, handlebars, and gears can be initially dissected.
You might, however, further divide each component into its component parts. For instance, artificial intelligence can be further divided into machine learning, deep learning, computer vision, and natural language processing.
This step also helps you to build deeper knowledge of the problem by identifying all of the components in depth.
2. Pattern Recognition
In the second stage, known as pattern recognition, the problem’s commonalities and trends are found.
There is a strong likelihood that they can be handled utilizing similar, or recurrent procedures if certain difficulties are similar in nature—both within the problem now being addressed and within previous problems.
This is a crucial element for developing effective solutions and ultimately saving you time.
Consider the following scenario: you are requested to develop a small program that draws a square. Instead of writing out the instruction four times in a row, the pattern of drawing a line and turning the pen 90 degrees can be repeated four times in a loop.
Pattern recognition is a critical talent for developing efficient and effective solutions to issues.
The identification of important elements of the solution is done at the third step of abstraction.
It necessitates the capacity to filter out superfluous parts of an issue so that you just concentrate on the crucial elements, as opposed to looking at precise specifics.
Another excellent example is when you play sports, you attempt to concentrate on the strategies you should use, and you ignore any teasing from your opponents.
Prior to developing the final solution, abstraction enables you to take into account all the important factors while disregarding any superfluous elements.
4. Algorithm Design
The creation of a thorough set of step-by-step instructions that describe how to solve the problem occurs during the Algorithm Design stage, the last phase in the Computational Thinking process.
An effective algorithm is one that can be given to someone else and followed without further explanation.
The world is full of algorithms, whether you’re cooking from a recipe, assembling flat-pack furniture, eating at a drive-through restaurant, or paying for your groceries at a self-service counter
Debugging is a crucial ability to master since it is an additional process involved in algorithm creation. Identification and correction of algorithmic flaws are referred to as debugging.
Debugging is a transferable ability that can be acquired across the curriculum by acting on and offering feedback, much like the other components of computational thinking. We can understand our surroundings with the aid of algorithms.
To sum up, the following generation of data scientists has to acquire the abilities that will enable them to more successfully adjust to the evolving work market and developing digital economy.
Future data scientists will find computational thinking to be a useful tool as they continually alter their positions to accommodate the advancement of technology and more interoperability between people and machines.
In the end, computational thinking is essential for everyone in their day-to-day tasks.