Data science is about solving real-world problems, so it’s unsurprising that certain skills are a valuable asset in their ever-evolving toolkit. Computational thinking should be a core educational path for any aspiring data scientist as it involves basic concepts regarding computer science and how to use abstraction and decomposition when addressing complex problems.
In the digital-first era, computational thinking is a critical skill, not only for future data scientists but for anyone who wants to participate in the computational world. According to the U.S. Bureau of Labor Statistics, demand for data science-related jobs, such as computer and information research scientists, will grow 22 percent from 2020 to 2029 — faster than any other occupation.
To prepare for the future of work and the progression of the job market that is being shaped by pervasive automation, artificial intelligence, and machine learning, computational thinking skills must be emphasized as a core component of education and career development.
What is Computational Thinking?
Computational thinking is a "problem-solving process" in which the brain is trained to assess and discover a reasonable solution to a given problem. In basic terms, it is the capacity to organise an issue and employ many faculties, such as arts, creativity, and problem-solving, to build a solution. Understanding how a computer solves a problem entails following basic actions one after the other that are ideally repeated in nature.
Computational Thinking Explained
Occasionally referred to as algorithmic thinking, computational thinking is a systematic approach towards solving a complicated problem by breaking it down into smaller, simpler steps in a way that it can be executed by a computer or machine. Solving a problem in a way that a computer can execute the process is important because it means that the solution can be applied towards similar problems in other scenarios. Part of computational thinking is embracing an agile, creative, and adaptable mindset to work through problems and potential solutions most efficiently, and using and interpreting data effectively.
Computational thinking originates from the type of thinking utilized by computer scientists, but is acknowledged as a method of thinking that anyone can use to solve problems in both their personal or professional life. So the point isn’t to apply reasoning that mimics computers, but to develop problem-solving techniques that computer scientists typically rely on.
Numerous data-intensive and quantitative problems can be solved through computational thinking, which makes this a significant asset for data scientists. This approach can be applied for problem solving across numerous areas like artificial intelligence and mathematics, for example. The programming language Python is also important in this process as it’s used in the statistical analysis process to express the solution on a computer.
Generally, there are four pillars of computational thinking:
- Decomposition: Reducing a complex problem into more negligible parts.
- Pattern recognition: Searching for any similarities throughout the problems.
- Abstraction: Honing in on only the most crucial information and disregarding the more inconsequential pieces of information.
- Algorithms: Creating a deliberate, step-by-step solution or rules to address the problem.
Usually computational thinking can be applied to highly structured problems; for example, the framework for the specific problem demands a highly structured solution, like an algorithm. Recurrent problems are also ideal for a computational thinking approach.
Educational systems are focusing on offering curriculums that incorporate computational thinking practices. New Zealand, Australia, South Africa, and other countries have already started to incorporate computational thinking into curriculums for grades K-12, and educational systems across the U.S. are increasingly featuring courses around computational thinking, as well.
As our world becomes more interconnected and moves towards a digital economy, critical thinking skills and mindsets will be essential to supporting new ways of working and engaging with technology to fuel innovative new services, solutions, and tools in multiple contexts.
Computational Thinking Skills
Computational thinking skills necessitate exhaustively researching and analysing problems in order to comprehend them properly. To express both issues and answers, use clear and descriptive language. At each level of the process, provide explicit logic.
Computational Thinking Techniques
We have developed seven powerful computational thinking tactics to empower young innovators with crucial problem-solving abilities to help kids become future-ready. Students who follow these suggestions will gain essential skills and be prepared for the future.
Look, Listen, and Learn (Collect Data)
The first step in tackling any problem is figuring out what you know. Computational thinking entails determining which data sources to use and which data are the most relevant. To solve a math problem, for example, students could collect quantitative data on a phenomenon and then apply mathematical tools to determine how to tackle the problem.
Ask Questions (Analyze Data)
When we evaluate data points, it is simpler to grasp how they fit into a broader problem's context. A fantastic technique to test a hypothesis is to use visuals such as charts and statistical tools. Students should make charts and visuals to support their views and communicate facts. Students must assess the facts they have gathered in order to identify what is significant in demonstrating their hypothesis or essential concepts.
Understand the Problem (Find Patterns)
One advantage of having data to work with is finding patterns within it. Making predictions and developing guidelines to address additional issues becomes simpler when patterns are discovered. Students can predict what will happen next by detecting themes and connections.
Need to Know (Decompose Problems)
Students should be able to handle challenging problems. It is simpler to discover answers when challenges are broken down into manageable bits. Deconstructing problems into smaller components makes it easier to consume and classify information.
When presented with a problem, encourage students to consider the larger picture. They will be left with a solution that will work for various challenges if they spot commonalities and remove details.
Create a Prototype (Build Models)
Encourage students to test, alter, and enhance ideas by utilising design tools to forecast results while brainstorming how to construct an effective model. They will save time and obtain a complete knowledge of their modelled concepts before developing them in real life.
Highlight and Fix (Develop Algorithms)
Consider problem solving to be a road plan for completing a task. When students can construct answers with step-by-step instructions, they create an algorithm that may be used for future issues.
Computational Thinking Examples
Our daily lives are replete with tasks that need computational thinking. Looking around, we can see a slew of issues that need to be addressed. Computational thinking is employed in the real world in any endeavour requiring a sequence of steps. That can range from buying automobiles to booking movie tickets and dinner tables.
Consider the case of purchasing a new automobile within your budget. First, we'll divide the funds and identify the various possibilities. Based on facts and consensus, we now choose the fuel type and four-wheeler colour. As a result, we eliminate any possibilities that are no longer relevant. We proceed to the next phase of reviewing expert reviews and must-have features. We go for a test drive to choose the best alternative.
Simple actions like going to the movies and eating dinner need computational thought. We must choose a restaurant and a movie show and time them such that both are noticed.
Decomposition in Computational Thinking
Decomposition is the first step in computational thinking. It is the process of dividing intricate statements into smaller bits. This makes achieving the desired outcome simple and achievable.
Knowing the decomposition technique is vital because it allows you to grasp the minor details of a larger image. Through it, a child learns to think like a machine and solve complex issues.
A medical student, for example, knows the operation of many human body organs in order to comprehend the human body as a whole.
In contrast, a mathematics student breaks down an equation into clear-cut components and solves it in parts to obtain the solution.
It works the same way in computer science while creating a game. As a result, the designer must create the character, narrative, actions, and so on for the game to function.
Pattern Recognition in Computational Thinking
Another part of this skill is pattern recognition. Pattern recognition depends on a child's ability to interpret objects and visuals.
A child can observe the similarities and differences. And any child may think of the next move using this talent. It significantly influences a child's capacity to mix multiple patterns and produce the following outcome.
Pattern recognition is quite valuable for making certain judgements. It gets easier to deal with various situations.
Pattern recognition is critical in computers. Artificial intelligence has lately made significant advances. Neural Networks are one such subject where we may find patterns in work.
A neural network is a set of algorithms that detect underlying relationships in a batch of data using a technique similar to how the human brain works. We can observe how a computer detects and employs patterns to work. Similarly, by studying patterns, children may make decisions and produce results.
Abstraction in Computational Thinking
One of the pillars of computer science or computational thinking is abstraction. It is the process of removing unneeded parts from anything. It just retains what is necessary in place.
It is about retaining valuable data and an item in order to decrease complexity and improve performance. It assists us in prioritising tasks and carrying them out carefully. We may simplify an issue by using abstraction. It maintains what is essential and discards what isn't.
This allows us to solve issues quickly without giving undue weight to unimportant details. It provides us with an object's significance and clarity.
For instance, if you want to study a topic from a book, will you read the entire book? Isn't that correct? You will only read about that one topic. In practice, this is how abstraction works.
Algorithms in Computational Thinking
One of the most fundamental cornerstones of this skill is algorithms. Algorithmic thinking aids in the development of a problem solution. The use of specific steps accomplishes it.
In general, algorithmic thinking entails addressing a problem by designing some defined procedures that must be taken. Algorithms are rules developed by a single person to tackle comparable situations.
For instance, schools teach students how to execute division and multiplication. The algorithms are what they learn.
Learners who understand multiplication algorithms can execute multiplication on any given integer. Algorithms enable systems to self-automate without establishing new patterns.
Micro-Credential for Computational Thinking
Well, a micro-credential is basically a digitalised certificate that confirms a person's proficiency in a specific skill or combination of abilities. Teachers must present proof of student work from classroom activities and documentation of reflection and lesson planning to obtain a micro-credential. Because most instructors need to familiarise themselves with integrating computational thinking, micro-credentials can benefit professional growth and/or credentialing paths. Simplilearn has developed micro-credentials for Computational Thinking Practices. Because the extent to which students have developed core abilities can only be judged once they are manifested through applied practices, these micro-credentials are structured around practices.
Computational Thinking for an Evolving Future of Work
The next generation of data scientists needs to develop the skills that will help them better adapt to the changing job market and emerging digital economy. Computational thinking will prove to be a valuable resource for future data scientists who must continuously reshape their roles around the progression of technology and closer integration between humans and machines.