- Terminology (A to D)
- AI Capability Control
- Asset Performance
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Storytelling
- Data Science
- Data Warehousing
- Decision Tree
- Deep Learning
- Deep Reinforcement Learning
- Diffusion Models
- Digital Twin
- Dimensionality Reduction
- Terminology (E to K)
- Edge AI
- Emotion AI
- Ensemble Learning
- Ethical Hacking
- Explainable AI
- Federated Learning
- Generative AI
- Generative Adversarial Network
- Generative vs. Discriminative
- Gradient Boosting
- Gradient Descent
- Few-Shot Learning
- Image Classification
- IT Operations (ITOps)
- Incident Automation
- Influence Engineering
- K-Means Clustering
- K-Nearest Neighbors
- Terminology (L to Q)
- Terminology (R to Z)
Table Of Contents
The era we live in requires new ways of thinking to solve new sets of challenges. The rise of artificial intelligence (AI) and machine learning (ML) has ushered in a new data-driven world, which requires innovative approaches.
Computational thinking, often referred to as CT, is a problem-solving technique that computer programmers use when writing computer programs and algorithms. In the case of programmers, they break down complex problems into more bite-sized pieces, which makes it easier to fully understand them and develop solutions that work for both computers and humans.
Humans also apply the computational thinking technique outside of programming. They break down problems into smaller and simpler fragments before outlining solutions to address each individual problem. This makes the problem-solving process far easier to comprehend.
There are a few requirements for computational thinking. For one, it requires you to explore and analyze problems thoroughly to understand them. It also requires you to use precise language when outlining both problems and solutions, and you should apply clear reasoning at every stage of the process.
Computational thinking helps us break down any given problem in a systematic manner, which enables us to develop and describe solutions in simple terms that can be either executed by a human or computer.
Four Key Techniques of Computational Thinking
When looking at computational thinking, there are four key techniques that should be understood:
- Decomposition: breaking down complex problems into smaller, more manageable pieces.
- Pattern Recognition: identifying similarities among and within problems.
- Abstraction: focusing on important information while leaving out irrelevant details.
- Algorithms: developing a step-by-step solution or certain rules that should be followed to solve the problem.
Each one of these techniques is just as important as the next. If you’re missing one, then the entire system is likely to collapse.
When attempting to solve a complex problem, it’s not easy to get started. With computational thinking, the decomposition process involves breaking it down into small, more manageable problems. You can then look at each one of the smaller problems individually and consider how similar problems have been previously solved, which is the pattern recognition technique. You then use the abstraction technique to focus only on the important details while leaving out irrelevant information. Finally, the algorithm technique helps you establish simple rules to solve each of the smaller problems.
All of these steps or rules are combined to program a system and help it efficiently solve a complex problem.
Importance of Computational Thinking
It’s important to recognize that computational thinking is not the same as programming. While programming tells a computer what to do and how to do it, computational thinking helps you determine what to tell the computer to do. In other words, computational thinking involves planning while programming involves following directions.
Another critical aspect of computational thinking is that it’s not just for computer scientists or programmers. It is a fundamental skill that everyone should possess, and it includes a range of mental tools that reflect the wide breadth of the computer science field.
While it is an important technique that enables computer scientists to better work with data, understand systems, and create workable computation models, it is also applied in real-world applications outside of computer science. Computational thinking is a highly effective tool for students and learning, helping develop problem-solving strategies that can be applied in everyday life. When this everyday life continues to include more and more digital aspects, computational thinking concepts become even more important to help people solve a wide range of challenges in an effective and manageable way.
The benefits and importance of computational thinking is why it is being taught as a national curriculum in education systems, which is the case in the United States and United Kingdom.
Sectors and Fields Using Computational Thinking
Computational thinking is applicable to everyone and anyone. It is an empowering tool for managers, decision makers, and administrators looking to think laterally and generate a wide range of solutions and adapt their strategy. Technical teams can use computational thinking to optimize their techniques.
Here are some of the top fields that rely on computational thinking:
- Engineering: used to design, simulate, model, optimize, and predict the behavior of systems under a variety of conditions.
- Healthcare: provides new analysis methods for medical data and helps develop more efficient systems that help make better decisions.
- Finance: used to explore market behavior and manage insurance claims.
- Data Science: combines with modern analytical techniques to provide better, more quantifiable answers where traditional techniques normally fail.
- Science: helps automate the importing of data and enables high-powered analysis.
- Media: used to model natural events, create animations, design patterns, generate 3D sculptures, and more.
- Law and Social Sciences: used to analyze social networks, model behaviors, and perform analysis of socioeconomic data.
- Environment: helps with climate change prediction, alternative energy development, pollution modeling, and planning for preservation.
- Communications and Security: makes it easier to develop algorithms for efficient information transfer and data security.
Computational Thinking in Today’s Business
Computational thinking is crucial to today’s business, and it will be a core skill for future job opportunities. As technology continues to become more sophisticated, we are required to understand how to collect and filter data.
We also need to know where to look for data and how to use it in decision making. When faced with complex problems, people need to have the confidence and ability to come up with logical solutions, and this can be provided by computational thinking, which is flexible and provides a consistent technique.
The modern workplace is built upon problem solving, which defines an organization’s success or failure. Computational thinking enables both business leaders and employees to deliver solutions in a smarter and more economical way. Modern day businesses are required to work smarter and deliver solutions faster, and by equipping your workforce with computational thinking skills, you can accelerate your productivity and take the business to the next level.
Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.