5+ Parts of Computational Thinking Every Coder Should Know (in 2024)

Whether you work as a programmer, analyst, or DevOps, problem-solving is key to your career success.

You might have heard about computational thinking and how it assists in getting the best answers from your analysis.

When you’re ready to level up your career, becoming a computational thinking expert can only help impress those who judge your work.

In this article, you will learn about computational thinking, ways of approaching its methodology, and careers that rely on expertise.

TL;DR:

Computational thinking is a way of thinking about problems and solutions.

It allows you to vett problems and solutions through several cognition tools that help you hone in on answers.

Computational thinking also makes it easier to codify your problems and solutions to use software to assist in finding answers.

The 5+ components of computational thinking are:

1. Decomposition: Breaking down a complex problem into smaller, more manageable sub-problems.
2. Pattern Recognition: Identifying similarities or patterns in data that can help solve the problem.
3. Abstraction: Focusing on the essential details while ignoring irrelevant information, allowing you to define the core aspects of the problem.
4. Algorithmic Thinking: Developing a step-by-step sequence of instructions or rules to solve the problem.
5. Solution Evaluation: Systematically testing and improving solutions. This involves identifying pros and cons, comparing tradeoffs, and refining approaches.
6. Generalization: Extending a solution to apply to a broader range of problems. Identifying the general principles that underlie a specific problem’s solution.

What is Computational Thinking?

Computational thinking refers to the thought processes involved in formulating problems and their solutions in ways that allow computers and other tools to be used to help solve them.

Computational thinking provides a problem-solving framework that can be applied to many subjects and disciplines.

It complements mathematical and engineering thinking with the use of computers and coding.

The goal is to train students to tackle complex problems in practical ways suitable for computerized solutions.

The 5+ components of computational thinking are:

1. Decomposition: Breaking down a complex problem into smaller, more manageable sub-problems.
2. Pattern Recognition: Identifying similarities or patterns in data that can help solve the problem.
3. Abstraction: Focusing on the essential details while ignoring irrelevant information, allowing you to define the core aspects of the problem.
4. Algorithmic Thinking: Developing a step-by-step sequence of instructions or rules to solve the problem.
5. Solution Evaluation: Systematically testing and improving solutions. This involves identifying pros and cons, comparing tradeoffs, and refining approaches.
6. Generalization: Extending a solution to apply to a broader range of problems. Identifying the general principles that underlie a specific problem’s solution.

1 – Decomposition

Decomposition is essential for managing complexity:

• break down large problems
• treat sub-problems as independent problems
• identify pieces of the problem
• see how problem pieces relate
• sort sub-problems into hierarchies
• iterate through problems to refine solutions
• abstract problems to filter out unnecessary details
• decomposition methods include top-down, bottom-up, and modular

Decomposition breaks down a complex problem or system into smaller, more manageable parts.

It is a crucial concept in computational thinking.

Decomposition allows you to break a significant problem down into sub-problems that are easier to solve.

Decomposition requires analyzing how the parts relate, depend on each other, and contribute to the overall goal.

Sub-problems can be solved independently, and their solutions combined later to solve the overall problem.

It involves identifying the component pieces of a problem and figuring out how they relate and interact with each other.

Hierarchical decomposition involves breaking a problem down into sub-problems and then breaking those down further. This creates a hierarchical tree structure of problems.

The sub-problems should represent discrete, modular pieces that can be addressed separately but help solve the whole when combined.

It is an iterative process: solutions generate new understanding, which leads to refining the decomposition to improve solutions.

Abstraction is vital to decomposition: you identify key aspects to focus on and ignore unnecessary details.

Decomposition strategies include top-down, bottom-up, and modular decomposition.

The goal is to break a problem into bite-sized pieces that can be efficiently solved and combined into an overall solution.

2 – Pattern Recognition

Pattern recognition is identifying similarities, recurring themes, or regularities in data.

Pattern recognition in computational thinking aims to identify useful patterns that reveal simplifying structures and opportunities for abstraction.

This guides better problem decomposition, algorithms, and, ultimately, problem-solving.

• use patterns to find structures, trends, and relationships
• find underlying algorithms and data patterns
• abstract patterns into predictive models
• categorize and classify problems
• use coding loops to create reusable patterns
• create and use libraries
• visualize with graphs and charts
• program machine learning algorithms to detect patterns
• decompose problems according to patterns
• use marks to find preconceived biases

Finding patterns helps reveal functional structures, trends, or relationships within complex problem spaces.

Patterns can suggest underlying rules or algorithms that govern systems. Identifying these rules can lead to predictive models.

Categories and classifications allow the grouping of problems or data by common patterns. This abstraction and generalization enables simplicity.

In coding, control structures like loops let you abstract repeating code patterns into reusable forms.

Creating libraries of reusable functions and classes allows you to encode common patterns to avoid redundant coding.

Pattern recognition using data visualization techniques (charts, graphs, etc.) can allow patterns to surface in complex data.

Machine learning algorithms automatically detect patterns in data that can be used for predictive modeling and analysis.

Problems can be decomposed by identifying patterns amongst sub-problems to solve them more efficiently.

New patterns can emerge from combinations of other ways when evaluating sub-solutions.

Sometimes, breaking down preconceived patterns is critical to further insight into a problem.

3 – Abstraction

Abstraction in computational thinking refers to reducing complexity by focusing on the key details and ignoring irrelevant information.

It allows you to develop generalized solutions that apply more broadly.

Abstraction involves identifying the essence or underlying structure of a problem to filter out unnecessary details.

It simplifies problems by modeling only the relevant qualities and behaviors.

Abstract representations help deal with complexity by hiding lower-level details.

For example, functions and procedures abstract away implementation details.

• abstract patterns to create reusable code
• hide unnecessary details
• hierarchical abstraction represents lower-level reality
• expose attributes and behaviors
• control abstraction identifies behaviors
• repeat abstraction turns patterns into reusable code

Abstracting standard features or patterns across problems allows the development of more general reusable solutions.

Hierarchical abstraction builds layered levels, where higher levels hide implementation details of lower levels. For example, machine code abstracts hardware details.

Data abstraction structures data only to expose operationally relevant attributes and behaviors. For example, object-oriented classes.

Control abstraction manages complexity through the abstraction of execution control, like in structured programming.

Repeat abstraction extracts standard procedures and patterns into reusable code.

Abstracting problems allows focusing on conceptual essence rather than particular instances.

This conceptualization leads to generalized knowledge.

Finding the right level of abstraction is key. Too little abstraction leaves unnecessary detail, and too much removes essential nuances.

4 – Algorithms

Algorithms are one of the core aspects of computational thinking.

An algorithm is a step-by-step procedure for solving a problem in a finite number of steps.

Algorithms provide precise, unambiguous, mechanical instructions for accomplishing a task.

Breaking problems down via decomposition allows for identifying sub-problems that can be solved algorithmically.

• use abstraction algorithmically to focus on relevant components
• solve problems step-by-step
• efficiently focus on the most relevant information
• algorithmic design patterns include iteration, divide and conquer, transformation, greedy approach, and dynamic programming
• measure input to predict output
• use procedural steps to get from A to B
• test algorithms for accuracy and efficiency
• algorithm types include sorting, graph search, compression, and machine learning

Algorithms rely on abstraction — ignoring unnecessary details and focusing only on relevant information.

Efficiency is key. Algorithms should provide the most effective process for solving the problem with the smallest computational resources.

Algorithms require precise definitions of the inputs and outputs and step-by-step instructions for getting from input to output.

Algorithm design techniques involve iteration, induction, divide and conquer, transformations, greedy approaches, and dynamic programming.

Algorithmic thinking means developing the ability to think through well-defined inputs, outputs, and procedural steps to get from one to the other.

Testing and analyzing algorithms involves looking at correctness, efficiency, resource requirements, and potential errors.

Algorithm examples include sorting algorithms, graph search algorithms, compression algorithms, and machine learning algorithms.

5 – Solution Evaluation

Evaluating solutions is a critical part of the computational thinking process.

Solution evaluation allows you to test different inputs and edge cases to reveal errors, weaknesses, and limitations.

The goal of solution evaluation is to take an iterative, analytical approach to evaluate a solution to systematically improve and refine it rigorously.

Evaluation is not just about proving correctness but enhancing quality.

• use profiling to evaluate for efficiency, speed, and complexity
• evaluate code for readability and maintainability
• tradeoff analysis evaluates strengths and weaknesses
• consider a solution’s integration with other solutions
• use refactoring to find faults and improve quality
• evaluate whether the code conforms to best practices
• analyze security, failure, and invalid inputs
• use peer reviews to evaluate code
• use metrics to quantify the evaluation

Evaluation quantifies metrics such as correctness, usability, security, and reliability.

Solution evaluation looks at speed, memory use, and computational complexity.

To evaluate a code solution, you look at the code’s readability, understandability, and maintainability.

In tradeoff analysis, you compare different solutions for strengths and weaknesses.

You will also examine how well the solution integrates with existing code and systems.

Evaluating solutions involves generalizing a solution for broader uses.

Profiling solutions identify performance bottlenecks, memory issues, or other quantitative metrics.

Refactoring and redesigning the solution for improvements, even if correct, enhances the solution’s quality.

A simple solution evaluation checks code against standards, best practices, and specifications.

You also look at security concerns, failure modes, and invalid inputs.

You evaluate how a decomposition, abstraction, or algorithm can be improved.

Evaluation leverages peer reviews for additional feedback on the solution.

6 – Generalization

In generalizing, you extend a solution to apply more broadly to a class of related problems.

Generalization identifies patterns and standard features across problems to allow for the extraction of shared abstract concepts.

Generalizing problems leads to generalized solutions and insights that apply broadly.

• extend a key to related domains
• find patterns to extract abstract concepts
• iterate and reiterate over steps to find abstractions
• find what doesn’t change to generalize
• create reusable templates and modules
• parameterize functions to adapt to different contexts
• identify assumptions that don’t apply to the problem
• refactor (decompose) to separate instances from reusable parts

You can generalize solutions, models, and algorithms to use a solution to a class of related probelms.

Generalizing means widening the context and scope little by little to expand the applicability of the concepts.

It is an iterative process. Each generalization step can lead to even broader abstractions and wider applicability.

Invariant aspects that stay constant even as problems vary are the basis of generalization.

One form of conception is to abstract specific examples into general reusable code, templates, or modules.

Another is to find ways to parameterize algorithms or code to make them configurable and adaptable to different contexts.

Generalization allows you to identify assumptions or constraints that limit the generality of a solution and remove them.

Generalization uses decomposition to find reusable parts. Refactoring problems and solutions is separating instance-specific details from reusable generalized components.

How Computational Thinking Started and Evolved

• Seymour Papert coined “computational thinking.”
• Papert advocated teaching step-by-step thinking to solve problems
• Papert and Marvin Minsky created LOGO to teach coding to kids.
• Papert and Minsky were artificial intelligence pioneers
• Wing and Aho popularized computational thinking
• Computational thinking applies to any field, including physics, linguistics, and biology.
• Computational thinking uses logic, math, critical thinking, and the scientific method.

Computational thinking is generally attributed to a mathematician, computer scientist, and educator, Seymour Papert.

In 1980, Papert published the book “Mindstorms: Children, Computers, and Powerful Ideas” which introduced ideas around computational thinking in education.

In it, he argued that students should learn to think like computers in a procedural, step-by-step manner to problem-solve effectively.

Papert built on the work of Jean Piaget’s theories of child development and programming language development work by Alan Perlis and others.

He collaborated with Marvin Minsky in co-founding the MIT Artificial Intelligence Lab and worked on the programming language Logo to teach kids coding.

Papert and Minsky pioneered artificial intelligence research and viewed coding as a new, learnable literacy.

Around 2000, computer scientists Jeannette Wing and Alfred Aho popularized the term “computational thinking” in Papert’s earlier work.

Wing highlighted computational thinking’s applicability across subjects beyond computer science.

Aho connected it to traditional forms of analytical thinking and problem-solving.

Wing Took Computational Science to Medicine and Psychology

Jeannette Wing suggested that computational thinking could be applied across diverse fields beyond computer science.

Researchers can use computational thinking in medicine to model disease outbreaks and simulate public health interventions to contain them. We can analyze and visualize complex medical data to find cures.

Financial Technology can use computational to create financial models that identify trends and optimize investment strategies.

Political interests can use computational analysis on polling data to more accurately predict election outcomes.

Artists can apply computing principles like abstraction to create minimalist designs.

Engineers can decompose complex systems to find hierarchical models with clear interfaces.

Social scientists can analyze human communication and culture using machine learning to identify trends.

Biologists can leverage bioinformatics to analyze DNA sequences and model protein folding with algorithms.

Architects can use computational design tools to create optimized, efficient building structures.

Using rules-based systems and statistical models, linguists can analyze language syntax, semantics, and phonology.

Psychologists can use computational thinking models to understand human cognition, brain function, and neural networks.

Aho Expanded Traditional Analysis and Problem Solving

Alfred Aho showed that computational thinking builds upon traditional analytical thinking.

It adds to our ability to use logic, math, and more.

Computational thinking utilizes logical thinking by requiring programmers to define clear, unambiguous rules and procedures.

Programming languages have firmly defined semantics.

Logical thinking enables the creation of correct algorithms.

Computational thinking builds on mathematical reasoning, such as inductive and deductive logic, through mathematical models.

Algorithm design uses mathematical techniques such as recursion, transforms, and proof techniques.

Software engineering leverages computational thinking by applying modularity, separation of concerns, and abstraction to tame complexity.

Programming uses an engineering mindset.

The scientific method aligns with computational thinking in hypothesis formulation, experimental design, data-driven analysis, and modeling.

Simulation science uses computational models to study systems.

Developers must critically analyze problems to create effective decompositions, abstractions, and algorithmic solutions.

Critically evaluating optimizations and tradeoffs is critical.

Computational methods provide concrete problem-solving techniques applicable across many disciplines.

Algorithmic thinking, in particular, helps structure generalized solutions.

Why Computational Thinking is So Important Now

Computational thinking has been gaining popularity and attention for a few key reasons.

• society is now digital
• computational thinking helps you understand digital systems
• science, business, and social problems are getting more complex
• we have more data than ever
• data needs to be turned into information
• everyone with a phone or computer has computational power
• the need for computational thinking creates jobs in software development, data science, and robotics
• programming is an essential skill, like reading
• technology companies value and pay for computational thinking skills

As our society becomes highly digital, thinking computationally is becoming a core capability needed to understand, interface with, and advance technology for the future.

Computational thinking provides skills to operate and understand these systems.

Problems across disciplines like science, business, and social issues are becoming more complex and data-driven, benefitting from computational analysis.

The proliferation of computers, smartphones, and the internet means computational power is ubiquitous.

Computational thinking leverages this.

Vast data are generated, necessitating new computational tools and analysis techniques.

Fields like machine learning and artificial intelligence are advancing rapidly, powered by computational methods.

Jobs involving software development, data science, robotics, and other computer-based careers are snowballing and require this thinking.

Programming is becoming an essential literacy and critical thinking skill on par with reading, writing, and arithmetic.

A more comprehensive range of accessible programming tools, languages, and environments now exist.

Educational institutions and policymakers recognize computational thinking as a crucial skill and are starting to integrate it into curriculums.

Technology companies are highly valued, and computational prowess is a 21st-century skill.

Computational thinking is a problem-solving approach that involves breaking down complex problems into smaller, more manageable parts and using algorithms and logical reasoning to solve them.

It is a skill widely used in computer science and other fields for problem-solving and decision-making.

Use Computational Thinking for Problem Solving

 Computational thinking helps individuals solve complex problems and make informed decisions.

It encourages a systematic and logical approach to problem-solving, which is transferable to various domains and can be used to solve real-world problems.

The 5+ Essential Components of Computational Thinking

1. Decomposition: Breaking down complex problems into smaller, manageable parts
2. Pattern recognition: Identifying patterns, similarities, or common themes that can inform solutions
3. Abstraction: Focusing on essential details and filtering out unnecessary information
4. Algorithmic thinking: Developing step-by-step instructions or procedures to solve problems
5. Solution evaluation: Systematically testing and analyzing solutions for improvements
6. Generalization: Extending solutions to apply more broadly to related problems

A working knowledge of computational thinking makes you good at solving problems.

This skill is highly valued in programming, robotics, and machine learning.

You can apply computational thinking to any field, including linguistics, biology, and psychology.