problem solving computer program

What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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What is Programming? A Handbook for Beginners

Welcome to the amazing world of programming. This is one of the most useful and powerful skills that you can learn and use to make your visions come true.

In this handbook, we will dive into why programming is important, its applications, its basic concepts, and the skills you need to become a successful programmer.

You will learn:

• What programming is and why it is important .
• What a programming language is and why it is important .
• How programming is related to binary numbers .
• Real-world applications of programming .
• Skills you need to succeed as a programmer .
• Tips for learning how to code .
• Basic programming concepts .
• Types of programming languages .
• How to contribute to open source projects .
• And more...

Are you ready? Let's begin! ✨  

🔹 What is Programming?

Did you know that computer programming is already a fundamental part of your everyday lives? Let's see why. I'm sure that you will be greatly surprised.

Every time you turn on your smartphone, laptop, tablet, smart TV, or any other electronic device, you are running code that was planned, developed, and written by developers. This code creates the final and interactive result that you can see on your screen.

That is exactly what programming is all about. It is the process of writing code to solve a particular problem or to implement a particular task.

Programming is what allows your computer to run the programs you use every day and your smartphone to run the apps that you love. It is an essential part of our world as we know it.

Whenever you check your calendar, attend virtual conferences, browse the web, or edit a document, you are using code that has been written by developers.

"And what is code?" you may ask.

Code is a sequence of instructions that a programmer writes to tell a device (like a computer) what to do.

The device cannot know by itself how to handle a particular situation or how to perform a task. So developers are in charge of analyzing the situation and writing explicit instructions to implement what is needed.

To do this, they follow a particular syntax (a set of rules for writing the code).

A developer (or programmer) is the person who analyzes a problem and implements a solution in code.

Sounds amazing, right? It's very powerful and you can be part this wonderful world too by learning how to code. Let's see how.

You, as a developer.

Let's put you in a developer's shoes for a moment. Imagine that you are developing a mobile app, like the ones that you probably have installed on your smartphone right now.

What is the first thing that you would do?

Think about this for a moment.

The answer is...

Analyzing the problem. What are you trying to build?

As a developer, you would start by designing the layout of the app, how it will work, its different screens and functionality, and all the small details that will make your app an awesome tool for users around the world.

Only after you have everything carefully planned out, you can start to write your code. To do that, you will need to choose a programming language to work with. Let's see what a programming language is and why they are super important.

🔸 What is a Programing Language?

A programming language is a language that computers can understand.

We cannot just write English words in our program like this:

"Computer, solve this task!"

and hope that our computer can understand what we mean. We need to follow certain rules to write the instructions.

Every programming language has its own set of rules that determine if a line of code is valid or not. Because of this, the code you write in one programming language will be slightly different from others.

💡 Tip: Some programming languages are more complex than others but most of them share core concepts and functionality. If you learn how to code in one programming language, you will likely be able to learn another one faster.

Before you can start writing awesome programs and apps, you need to learn the basic rules of the programming language you chose for the task.

💡 Tip: a program is a set of instructions written in a programming language for the computer to execute. We usually write the code for our program in one or multiple files.

For example, this is a line of code in Python (a very popular programming language) that shows the message "Hello, World!" :

But if we write the same line of code in JavaScript (a programming language mainly used for web development), we will get an error because it will not be valid.

To do something very similar in JavaScript, we would write this line of code instead:

Visually, they look very different, right? This is because Python and JavaScript have a different syntax and a different set of built-in functions .

💡 Tip : built-in functions are basically tasks that are already defined in the programming language. This lets us use them directly in our code by writing their names and by specifying the values they need.  

In our examples, print() is a built-in function in Python while console.log() is a function that we can use in JavaScript to see the message in the console (an interactive tool) if we run our code in the browser.

Examples of programming languages include Python, JavaScript, TypeScript, Java, C, C#, C++, PHP, Go, Swift, SQL, and R. There are many programming languages and most of them can be used for many different purposes.

💡 Tip: These were the most popular programming languages on the Stack Overflow Developer Survey 2022 :

There are many other programming languages (hundreds or even thousands!) but usually, you will learn and work with some of the most popular ones. Some of them have broader applications like Python and JavaScript while others (like R) have more specific (and even scientific) purposes.

This sounds very interesting, right? And we are only starting to talk about programming languages. There is a lot to learn about them and I promise you that if you dive deeper into programming, your time and effort will be totally worth it.

Awesome! Now that you know what programming is and what programming languages are all about, let's see how programming is related to binary numbers.

🔹 Programming and Binary Numbers

When you think about programming, perhaps the first thing that comes to your mind is something like the below image, right? A sequence of 0 s and 1 s on your computer.

Programming is indeed related to binary numbers ( 0 and 1 ) but in an indirect way. Developers do not actually write their code using zeros and ones.

We usually write programs in a high-level programming language, a programming language with a syntax that recognizes specific words (called keywords), symbols, and values of different data types.

Basically, we write code in a way that humans can understand.

For example, these are the keywords that we can use in Python:

Every programming language has its own set of keywords (words written in English). These keywords are part of the syntax and core functionality of the programming language.

But keywords are just common words in English, almost like the ones that we would find in a book.

That leads us to two very important questions:

• How does the computer understand and interpret what we are trying to say?
• Where does the binary number system come into play here?

The computer does not understand these words, symbols, or values directly.

When a program runs, the code that we write in a high-level programming language that humans can understand is automatically transformed into binary code that the computer can understand.

This transformation of source code that humans can understand into binary code that the computer can understand is called compilation .

According to Britannica , a compiler is defined as:

Computer software that translates (compiles) source code written in a high-level language (e.g., C++) into a set of machine-language instructions that can be understood by a digital computer’s CPU.

Britannica also mentions that:

The term compiler was coined by American computer scientist Grace Hopper , who designed one of the first compilers in the early 1950s.

Some programming languages can be classified as compiled programming languages while others can be classified as interpreted programming languages based on how to they are transformed into machine-language instructions.

However, they all have to go through a process that converts them into instructions that the computer can understand.

Awesome. Now you know why binary code is so important for computer science. Without it, basically programming would not exist because computers would not be able to understand our instructions.

Now let's dive into the applications of programming and the different areas that you can explore.

🔸 Real-World Applications of Programming

Programming has many different applications in many different industries. This is truly amazing because you can apply your knowledge in virtually any industry that you are interested in.

From engineering to farming, from game development to physics, the possibilities are endless if you learn how to code.  

Let's see some of them. (I promise you. They are amazing! ⭐) .

Front-End Web Development

If you learn how to code, you can use your programming skills to design and develop websites and online platforms. Front-End Web Developers create the parts of the websites that users can see and interact with directly.

For example, right now you are reading an article on freeCodeCamp 's publication. The publication looks like this and it works like this thanks to code that front-end web developers wrote line by line.

💡 Tip: If you learn front-end web development, you can do this too.

Front-End Web Developers use HTML and CSS to create the structure of the website (these are markup languages, which are used to present information) and they write JavaScript code to add functionality and interactivity.

If you are interested in learning front-end web development, you can learn HTML and CSS with these free courses on freeCodeCamp's YouTube Channel:

• Learn HTML5 and CSS3 From Scratch - Full Course
• Learn HTML & CSS – Full Course for Beginners
• Frontend Web Development Bootcamp Course (JavaScript, HTML, CSS)
• Introduction To Responsive Web Design - HTML & CSS Tutorial

You can also learn JavaScript for free with these free online courses:

• Learn JavaScript - Full Course for Beginners
• JavaScript Programming - Full Course
• JavaScript DOM Manipulation – Full Course for Beginners
• Learn JavaScript by Building 7 Games - Full Course

💡 Tip: You can also earn a Responsive Web Design Certification while you learn with interactive exercises on freeCodeCamp.

Back-End Web Development

More complex and dynamic web applications that work with user data also require a server . This is a computer program that receives requests and sends appropriate responses. They also need a database , a collection of values stored in a structured way.

Back-End Web Developers are in charge of developing the code for these servers. They decide how to handle the different requests, how to send appropriate resources, how to store the information, and basically how to make everything that runs behind the scenes work smoothly and efficiently.

A real-world example of back-end web development is what happens when you create an account on freeCodeCamp and complete a challenge. Your information is stored on a database and you can access it later when you sign in with your email and password.

This amazing interactive functionality was implemented by back-end web developers.

💡 Tip: Full-stack Web Developers are in charge of both Front-End and Back-End Web Development. They have specialized knowledge on both areas.

All the complex platforms that you use every day, like social media platforms, online shopping platforms, and educational platforms, use servers and back-end web development to power their amazing functionality.

Python is an example of a powerful programming language used for this purpose. This is one of the most popular programming languages out there, and its popularity continues to rise every year. This is partly because it is simple and easy to learn and yet powerful and versatile enough to be used in real-world applications.

💡 Tip: if you are curious about the specific applications of Python, this is an article I wrote on this topic .

JavaScript can also be used for back-end web development thanks to Node.js.

Other programming languages used to develop web servers are PHP, Ruby, C#, and Java.

If you would like to learn Back-End Web Development, these are free courses on freeCodeCamp's YouTube channel:

• Python Backend Web Development Course (with Django)
• Node.js and Express.js - Full Course
• Full Stack Web Development for Beginners (Full Course on HTML, CSS, JavaScript, Node.js, MongoDB)
• Node.js / Express Course - Build 4 Projects

💡 Tip: freeCodeCamp also has a free Back End Development and APIs certification.

Mobile App Development

Mobile apps have become part of our everyday lives. I'm sure that you could not imagine life without them.

Think about your favorite mobile app. What do you love about it?

Our favorite apps help us with our daily tasks, they entertain us, they solve a problem, and they help us to achieve our goals. They are always there for us.

That is the power of mobile apps and you can be part of this amazing world too if you learn mobile app development.

Developers focused on mobile app development are in charge of planning, designing, and developing the user interface and functionality of these apps. They identify a gap in the existing apps and they try to create a working product to make people's lives better.

💡 Tip: regardless of the field you choose, your goal as a developer should always be making people's lives better. Apps are not just apps, they have the potential to change our lives. You should always remember this when you are planning your projects. Your code can make someone's life better and that is a very important responsibility.

Mobile app developers use programming languages like JavaScript, Java, Swift, Kotlin, and Dart. Frameworks like Flutter and React Native are super helpful to build cross-platform mobile apps (that is, apps that run smoothly on multiple different operating systems like Android and iOS).

According to Flutter 's official documentation:

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase.

If you would like to learn mobile app development, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Flutter Course for Beginners – 37-hour Cross Platform App Development Tutorial
• Flutter Course - Full Tutorial for Beginners (Build iOS and Android Apps)
• React Native - Intro Course for Beginners
• Learn React Native Gestures and Animations - Tutorial

Game Development

Games create long-lasting memories. I'm sure that you still remember your favorite games and why you love (or loved) them so much. Being a game developer means having the opportunity of bringing joy and entertainment to players around the world.

Game developers envision, design, plan, and implement the functionality of a game. They also need to find or create assets such as characters, obstacles, backgrounds, music, sound effects, and more.

💡 Tip: if you learn how to code, you can create your own games. Imagine creating an awesome and engaging game that users around the world will love. That is what I personally love about programming. You only need your computer, your knowledge, and some basic tools to create something amazing.

Popular programming languages used for game development include JavaScript, C++, Python, and C#.

If you are interested in learning game development, you can take these free courses on freeCodeCamp's YouTube channel:

• JavaScript Game Development Course for Beginners
• Learn Unity - Beginner's Game Development Tutorial
• Learn Python by Building Five Games - Full Course
• Code a 2D Game Using JavaScript, HTML, and CSS (w/ Free Game Assets) – Tutorial
• 2D Game Development with GDevelop - Crash Course
• Pokémon Coding Tutorial - CS50's Intro to Game Development

Biology, Physics, and Chemistry

Programming can be applied in every scientific field that you can imagine, including biology, physics, chemistry, and even astronomy. Yes! Scientists use programming all the time to collect and analyze data. They can even run simulations to test hypotheses.

In biology, computer programs can simulate population genetics and population dynamics. There is even an entire field called bioinformatics .

According to this article "Bioinformatics" by Ardeshir Bayat, member of the Centre for Integrated Genomic Medical Research at the University of Manchester:

Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data.

Dr. Bayat mentions that bioinformatics can be used for genome sequencing. He also mentions that its discoveries may lead to drug discoveries and individualized therapies.

Frequently used programming languages for bioinformatics include Python, R, PHP, PERL, and Java.

💡 Tip: R is a programming "language and environment for statistical computing and graphics" ( source ).

An example of a great tool that scientists can use for biology is Biopython . This is a Python framework with "freely available tools for biological computation."

If you would like to learn more about how you can apply your programming skills in science, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
• R Programming Tutorial - Learn the Basics of Statistical Computing
• Learn Python - Full Course for Beginners [Tutorial]

Physics requires running many simulations and programming is perfect for doing exactly that. With programming, scientists can program and run simulations based on specific scenarios that would be hard to replicate in real life. This is much more efficient.

Programming languages that are commonly used for physics simulations include C, Java, Python, MATLAB, and JavaScript.  

Chemistry also relies on simulations and data analysis, so it's a field where programming can be a very helpful tool.

In this scientific article by Dr. Ivar Ugi and his colleagues from Organisch-chemisches Institut der Technischen Universität München, they mention that:

The design of entirely new syntheses, and the classification and documentation of structures, substructures, and reactons are examples of new applications of computers to chemistry.

Scientific experiments also generate detailed data and results that can be analyzed with computer programs developed by scientists.  

Think about it: writing a program to generate a box plot or a scatter plot or any other type of plot to visualize trends in thousands of measurements can save researchers a lot of time and effort. This lets them focus on the most important part of their work: analyzing the results.

💡 Tips: if you are interested in diving deeper into this, this is a list of chemistry simulations by the American Chemical Society. These simulations were programmed by developers and they are helping thousands of students and teachers around the world.

Think about it...You could build the next great simulation. If you are interested in a scientific field, I totally recommend learning how to code. Your work will be much more productive and your results will be easier to analyze.

If you are interested in learning programming for scientific applications, these are free courses on freeCodeCamp's YouTube channel:

• Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)

Data Science and Engineering

Talking about data...programming is also essential for a field called Data Science . If you are interested in answering questions through data and statistics, this field might be exactly what you are looking for and having programming skills will help you to achieve your goals.

Data scientists collect and analyze data in order to answer questions in many different fields. According to UC Berkeley in the article " What is Data Science? ":

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.

There are many powerful programming languages for analyzing and visualizing data, but perhaps one of the most frequently used ones for this purpose is Python.

This is an example of the type of data visualizations that you can create with Python. They are very helpful to analyze data visually and you can customize them to your fit needs.

If you are interested in learning programming for data science, these are free courses on freeCodeCamp's YouTube channel:

• Learn Data Science Tutorial - Full Course for Beginners
• Intro to Data Science - Crash Course for Beginners
• Build 12 Data Science Apps with Python and Streamlit - Full Course
• Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

💡 Tip: you can also earn these free certifications on freeCodeCamp:

• Data Visualization
• Data Analysis with Python

Engineering

Engineering is another field where programming can help you to succeed. Being able to write your own computer programs can make your work much more efficient.

There are many tools created specifically for engineers. For example, the R programming language is specialized in statistical applications and Python is very popular in this field too.

Another great tool for programming in engineering is MATLAB . According to its official website:

MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.

Really, the possibilities are endless.

You can learn MATLAB with this crash course on the freeCodeCamp YouTube channel .

If you are interested in learning engineering tools related to programming, this is a free course on freeCodeCamp's YouTube channel that covers AutoCAD, a 2D and 3D computer-aided design software used by engineers:

• AutoCAD for Beginners - Full University Course

Medicine and Pharmacology

Medicine and pharmacology are constantly evolving by finding new treatments and procedures. Let's see how you can apply your programming skills in these fields.

Programming is really everywhere. If you are interested in the field of medicine, learning how to code can be very helpful for you too. Even if you would like to focus on computer science and software development, you can apply your knowledge in both fields.

Specialized developers are in charge of developing and writing the code that powers and controls the devices and machines that are used by modern medicine.

Think about it...all these machines and devices are controlled by software and someone has to write that software. Medical records are also stored and tracked by specialized systems created by developers. That could be you if you decide to follow this path. Sounds exciting, right?

According to the scientific article Application of Computer Techniques in Medicine :

Major uses of computers in medicine include hospital information system, data analysis in medicine, medical imaging laboratory computing, computer assisted medical decision making, care of critically ill patients, computer assisted therapy and so on.

Pharmacology

Programming and computer science can also be applied to develop new drugs in the field of pharmacology.

A remarkable example of what you can achieve in this field by learning how to code is presented in this article by MIT News. It describes how an MIT senior, Kristy Carpenter, was using computer science in 2019 to develop "new, more affordable drugs." Kristy mentions that:

Artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.

Another example of a real-world application of programming in pharmacology is related to Python (yes, Python has many applications!). Among its success stories , we find that Python was selected by AstraZeneca to develop techniques and programs that can help scientists to discover new drugs faster and more efficiently.

The documentation explains that:

To save time and money on laboratory work, experimental chemists use computational models to narrow the field of good drug candidates, while also verifying that the candidates to be tested are not simple variations of each other's basic chemical structure.

If you are interested in learning programming for medicine or health-related fields, this is a free course on freeCodeCamp's YouTube channel on programming for healthcare imaging:

• PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning Course

Have you ever thought that programming could be helpful for education? Well, let me tell you that it is and it is very important. Why? Because the digital learning tools that students and teachers use nowadays are programmed by developers.

Every time a student opens an educational app, browses an educational platform like freeCodeCamp, writes on a digital whiteboard, or attends a class through an online meeting platform, programming is making that possible.

As a programmer or as a teacher who knows how to code, you can create the next great app that will enhance the learning experience of students around the world.

Perhaps it will be a note-taking app, an online learning platform, a presentation app, an educational game, or any other app that could be helpful for students.

The important thing is to create it with students in mind if your goal is to make something amazing that will create long-lasting memories.

If you envision it, then you can create it with code.  

Teachers can also teach their students how to code to develop their problem-solving skills and to teach them important skills for their future.

💡 Tip: if you are teaching students how to code, Scratch is a great programming language to teach the basics of programming. It is particularly focused on teaching children how to code in an interactive way.

According to the official Scratch website:

Scratch is the world’s largest coding community for children and a coding language with a simple visual interface that allows young people to create digital stories, games, and animations.

If you are interested in learning how to code for educational purposes, these are courses that you may find helpful on freeCodeCamp's YouTube channel:

• Scratch Tutorial for Beginners - Make a Flappy Bird Game
• Computational Thinking & Scratch - Intro to Computer Science - Harvard's CS50 (2018)
• Android Development for Beginners - Full Course

Machine Learning, Artificial Intelligence, and Robotics

Some of the most amazing fields that are directly related to programming are Machine Learning, Artificial Intelligence, and Robotics. Let's see why.

Artificial Intelligence is defined by Britannica as:

The project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Machine learning is a branch or a subset of the field of Artificial Intelligence in which systems can learn on their own based on data. The goal of this learning process is to predict the expected output. These models continuously learn how to "think" and how to analyze situations based on their previous training.

The most commonly used programming languages in these fields are Python, C, C#, C++, and MATLAB.

Artificial intelligence and Machine Learning have amazing applications in various industries, such as:

• Image and object detection.
• Making predictions based on patterns.
• Text recognition.
• Recommendation engines (like when an online shopping platform shows you products that you may like or when YouTube shows you videos that you may like).
• Spam detection for emails.
• Fraud detection.
• Social media features like personalized feeds.
• Many more... there are literally millions of applications in virtually every industry.

If you are interested in learning how to code for Artificial Intelligence and Machine Learning, these are free courses on freeCodeCamp's YouTube channel:

• Machine Learning for Everybody – Full Course
• Machine Learning Course for Beginners
• PyTorch for Deep Learning & Machine Learning – Full Course
• TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
• Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning
• Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
• Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard
• Deep Learning Crash Course for Beginners
• Advanced Computer Vision with Python - Full Course

💡 Tip: you can also earn a Machine Learning with Python Certification on freeCodeCamp.

Programming is also very important for robotics. Yes, robots are programmed too!

Robotics is defined by Britannica as the:

Design, construction, and use of machines (robots) to perform tasks done traditionally by human beings.

Robots are just like computers. They do not know what to do until you tell them what to do by writing instructions in your programs. If you learn how to code, you can program robots and industrial machinery found in manufacturing facilities.

If you are interested in learning how to code for robotics, electronics, and related fields, this is a free course on Arduino on freeCodeCamp's YouTube channel:

• Arduino Course for Beginners - Open-Source Electronics Platform

Other Applications

There are many other fascinating applications of programming in almost every field. These are some highlights:

• Agriculture: in this article by MIT News, a farmer developed an autonomous tractor app after learning how to code.
• Self-driving cars: autonomous cars rely on software to analyze their surroundings and to make quick and accurate decisions on the road. If you are interested in this area, this is a course on this topic on freeCodeCamp's YouTube channel.
• Finance: programming can also be helpful to develop programs and models that predict financial indicators and trends. For example, this is a course on algorithmic trading on freeCodeCamp's YouTube channel.

The possibilities are endless. I hope that this section will give you a notion of why learning how to code is so important for your present and for your future. It will be a valuable skill to have in any field you choose.

Awesome. Now let's dive into the soft skills that you need to become a successful programmer.

🔹 Skills of a Successful Programmer

After going through the diverse range of applications of programming, you must be curious to know what skills are needed to succeed in this field.

A programmer should be curious. Whether you are just starting to learn how to code or you already have 20 years of experience, coding projects will always present you with new challenges and learning opportunities. If you take these opportunities, you will continously improve your skills and succeed.

Enthusiasm is a key trait of a successful programmer but this applies in general to any field if you want to succeed. Enthusiasm will keep you happy and curious about what you are creating and learning.

💡 Tip: If you ever feel like you are not as enthusiastic as you used to be, it's time to find or learn something new that can light the spark in you again and fill you with hope and dreams.

A programmer must be patient because transforming an initial idea into a working product can take time, effort, and many different steps. Patience will keep you focused on your final goal.  

Programming can be challenging. That is true. But what defines you is not how many challenges you face, it's how you face them. If you thrive despite these challenges, you will become a better programmer and you could create something that could change the world.

Programmers must be creative because even though every programming language has a particular set of rules for writing the code, coding is like using LEGOs. You have the building-blocks but you need to decide what to create and how to create it. The process of writing the code requires creativity while following the established best practices.

Problem-solving and Analysis

Programming is basically analyzing and solving problems with code. Depending on your field of choice, those problems will be simpler or more complex but they will all require some level of problem-solving skills and a thorough analysis of the situation.

Questions like:

• What should I build?
• How can I build it?
• What is the best way to build this?

Are part of the everyday routine of a programmer.

Ability to Focus for Long Periods of Time

When you are working on a coding project, you will need to focus on a task for long periods of time. From creating the design, to planning and writing the code, to testing the result, and to fixing bugs (issues with the code), you will dedicate many hours to a particular task. This is why it's essential to be able to focus and to keep your final goal in mind.

Taking Detailed Notes

This skill is very important for programmers, particularly when you are learning how to code. Taking detailed notes can be help you to understand and remember the concepts and tools you learn. This also applies for experienced programmers, since being a programmer involves life-long learning.

Communication

Initially, you might think that programming is a solitary activity and imagine that a programmer spends hundreds of hours alone sitting on a desk.

But the reality is that when you find your first job, you will see that communication is super important to coordinate tasks with other team members and to exchange ideas and feedback.

Open to Feedback

In programming, there is usually more than one way to implement the same functionality. Different alternatives may work similarly, but some may be easier to read or more efficient in terms of time or resource consumption.

When you are learning how to code, you should always take constructive feedback as a tool for learning. Similarly, when you are working on a team, take your colleagues' feedback positively and always try to improve.

Life-long Learning

Programming equals life-long learning. If you are interested in learning how to code, you must know that you will always need to be learning new things as new technologies emerge and existing technologies are updated. Think about it... that is great because there is always something interesting and new to learn!

Open to Trying New Things

Finally, an essential skill to be a successful programmer is to be open to trying new things. Step out of your comfort zone and be open to new technologies and products. In the technology industry, things evolve very quickly and adapting to change is essential.

🔸 Tips for Learning How to Code

Now that you know more about programming, programming languages, and the skills you need to be a successful programmer, let's see some tips for learning how to code.

💡 Tip: these tips are based on my personal experience and opinions.

• Choose one programming language to learn first. When you are learning how to code, it's easy to feel overwhelmed with the number of options and entry paths. My advice would be to focus on understanding the essential computer science concepts and one programming language first. Python and JavaScript are great options to start learning the fundamentals.
• Take detailed notes. Note-taking skills are essential to record and to analyze the topics you are learning. You can add custom comments and annotations to explain what you are learning.
• Practice constantly. You can only improve your problem-solving skills by practicing and by learning new techniques and tools. Try to practice every day.

💡 Tip: There is a challenge called the #100DaysOfCode challenge that you can join to practice every day.  

• Always try again. If you can't solve a problem on your first try, take a break and come back again and again until you solve it. That is the only way to learn. Learn from your mistakes and learn new approaches.
• Learn how to research and how to find answers. Programming languages, libraries, and frameworks usually have official documentations that explain their built-in elements and tools and how you can use them. This is a precious resource that you should definitely refer to.
• Browse Stack Overflow . This is an amazing platform. It is like an online encyclopedia of answers to common programming questions. You can find answers to existing questions and ask new questions to get help from the community.
• Set goals. Motivation is one of the most important factors for success. Setting goals is very important to keep you focused, motivated, and enthusiastic. Once you reach your goals, set new ones that you find challenging and exciting.
• Create projects. When you are learning how to code, applying your skills will help you to expand your knowledge and remember things better. Creating projects is the perfect way to practice and to create a portfolio that you can show to potential employers.

🔹 Basic Programming Concepts

Great. If reading this article has helped you confirm that you want to learn programming, let's take your first steps.

These are some basic programming concepts that you should know:

• Variable: a variable is a name that we assign to a value in a computer program. When we define a variable, we assign a value to a name and we allocate a space in memory to store that value. The value of a variable can be updated during the program.
• Constant: a constant is similar to a variable. It stores a value but it cannot be modified. Once you assign a value to a constant, you cannot change it during the entire program.
• Conditional: a conditional is a programming structure that lets developers choose what the computer should do based on a condition. If the condition is True, something will happen but if the condition is False, something different can happen.
• Loop: a loop is a programming structure that let us run a code block (a sequence of instructions) multiple times. They are super helpful to avoid code repetition and to implement more complex functionality.
• Function: a function helps us to avoid code repetition and to reuse our code. It is like a code block to which we assign a name but it also has some special characteristics. We can write the name of the function to run that sequence of instructions without writing them again.

💡 Tip: Functions can communicate with main programs and main programs can communicate with functions through parameters , arguments , and return statements.

• Class: a class is used as a blueprint to define the characteristics and functionality of a type of object. Just like we have objects in our real world, we can represent objects in our programs.
• Bug: a bug is an error in the logic or implementation of a program that results in an unexpected or incorrect output.
• Debugging: debugging is the process of finding and fixing bugs in a program.
• IDE: this acronym stands for Integrated Development Environment. It is a software development environment that has the most helpful tools that you will need to write computer programs such as a file editor, an explorer, a terminal, and helpful menu options.

💡 Tip: a commonly used and free IDE is Visual Studio Code , created by Microsoft.

Awesome! Now you know some of the fundamental concepts in programming. Like you learned, each programming language has a different syntax, but they all share most of these programming structures and concepts.  

🔸 Types of Programming Languages

Programming languages can be classified based on different criteria. If you want to learn how to code, it's important for you to learn these basic classifications:

• High-level programming languages: they are designed to be understood by humans and they have to be converted into machine code before the computer can understand them. They are the programming languages that we commonly use. For example: JavaScript, Python, Java, C#, C++, and Kotlin.
• Low-level programming languages: they are more difficult to understand because they are not designed for humans. They are designed to be understood and processed efficiently by machines.

Conversion into Machine Code

• Compiled programming languages: programs written with this type of programming language are converted directly into machine code by a compiler. Examples include C, C++, Haskell, and Go.
• Interpreted programming languages: programs written with this type of programming language rely on another program called the interpreter, which is in charge of running the code line by line. Examples include Python, JavaScript, PHP, and Ruby.

💡 Tip: according to this article on freeCodeCamp's publication:

Most programming languages can have both compiled and interpreted implementations – the language itself is not necessarily compiled or interpreted. However, for simplicity’s sake, they’re typically referred to as such.

There are other types of programming languages based on different criteria, such as:

• Procedural programming languages
• Functional programming languages
• Object-oriented programming languages
• Scripting languages
• Logic programming languages

And the list of types of programming languages continues. This is very interesting because you can analyze the characteristics of a programming language to help you choose the right one for your project.

🔹 How to Contribute to Open Source Projects

Finally, you might think that coding implies sitting at a desk for many hours looking at your code without any human interaction. But let me tell you that this does not have to be true at all. You can be part of a learning community or a developer community.

Initially, when you are learning how to code, you can participate in a learning community like freeCodeCamp. This way, you will share your journey with others who are learning how to code, just like you.

Then, when you have enough skills and confidence in your knowledge, you can practice by contributing to open source projects and join developer communities.

Open source software is defined by Opensource.com as:

Software with source code that anyone can inspect, modify, and enhance.

GitHub is an online platform for hosting projects with version control. There, you can find many open source projects (like freeCodeCamp ) that you can contribute to and practice your skills.

💡 Tip: many open source projects welcome first-time contributions and contributions from all skill levels. These are great opportunities to practice your skills and to contribute to real-world projects.  

Contributing to open source projects on GitHub is great to acquire new experience working and communicating with other developers. This is another important skill for finding a job in this field.

Working on a team is a great experience. I totally recommend it once you feel comfortable enough with your skills and knowledge.

You did it! You reached the end of this article. Great work. Now you know what programming is all about. Let's see a brief summary.

🔸 In Summary

• Programming is a very powerful skill. If you learn how to code, you can make your vision come true.
• Programming has many different applications in many different fields. You can find an application for programming in basically any field you choose.
• Programming languages can be classified based on different criteria and they share basic concepts such as variables, conditionals, loops, and functions.
• Always set goals and take detailed notes. To succeed as a programmer, you need to be enthusiastic and consistent.

Thank you very much for reading my article. I hope you liked it and found it helpful. Now you know why you should learn how to code.

🔅 I invite you to follow me on Twitter ( @EstefaniaCassN ) and YouTube ( Coding with Estefania ) to find coding tutorials.

Developer, technical writer, and content creator @freeCodeCamp. I run the freeCodeCamp.org Español YouTube channel.

If you read this far, thank the author to show them you care. Say Thanks

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

Problem Solving

Foundations course, introduction.

Before we start digging into some pretty nifty JavaScript, we need to begin talking about problem solving : the most important skill a developer needs.

Problem solving is the core thing software developers do. The programming languages and tools they use are secondary to this fundamental skill.

From his book, “Think Like a Programmer” , V. Anton Spraul defines problem solving in programming as:

Problem solving is writing an original program that performs a particular set of tasks and meets all stated constraints.

The set of tasks can range from solving small coding exercises all the way up to building a social network site like Facebook or a search engine like Google. Each problem has its own set of constraints, for example, high performance and scalability may not matter too much in a coding exercise but it will be vital in apps like Google that need to service billions of search queries each day.

New programmers often find problem solving the hardest skill to build. It’s not uncommon for budding programmers to breeze through learning syntax and programming concepts, yet when trying to code something on their own, they find themselves staring blankly at their text editor not knowing where to start.

The best way to improve your problem solving ability is by building experience by making lots and lots of programs. The more practice you have the better you’ll be prepared to solve real world problems.

In this lesson we will walk through a few techniques that can be used to help with the problem solving process.

Lesson overview

This section contains a general overview of topics that you will learn in this lesson.

• Explain the three steps in the problem solving process.
• Explain what pseudocode is and be able to use it to solve problems.
• Be able to break a problem down into subproblems.

Understand the problem

The first step to solving a problem is understanding exactly what the problem is. If you don’t understand the problem, you won’t know when you’ve successfully solved it and may waste a lot of time on a wrong solution .

To gain clarity and understanding of the problem, write it down on paper, reword it in plain English until it makes sense to you, and draw diagrams if that helps. When you can explain the problem to someone else in plain English, you understand it.

Now that you know what you’re aiming to solve, don’t jump into coding just yet. It’s time to plan out how you’re going to solve it first. Some of the questions you should answer at this stage of the process:

• Does your program have a user interface? What will it look like? What functionality will the interface have? Sketch this out on paper.
• What inputs will your program have? Will the user enter data or will you get input from somewhere else?
• What’s the desired output?
• Given your inputs, what are the steps necessary to return the desired output?

The last question is where you will write out an algorithm to solve the problem. You can think of an algorithm as a recipe for solving a particular problem. It defines the steps that need to be taken by the computer to solve a problem in pseudocode.

Pseudocode is writing out the logic for your program in natural language instead of code. It helps you slow down and think through the steps your program will have to go through to solve the problem.

Here’s an example of what the pseudocode for a program that prints all numbers up to an inputted number might look like:

This is a basic program to demonstrate how pseudocode looks. There will be more examples of pseudocode included in the assignments.

Divide and conquer

From your planning, you should have identified some subproblems of the big problem you’re solving. Each of the steps in the algorithm we wrote out in the last section are subproblems. Pick the smallest or simplest one and start there with coding.

It’s important to remember that you might not know all the steps that you might need up front, so your algorithm may be incomplete -— this is fine. Getting started with and solving one of the subproblems you have identified in the planning stage often reveals the next subproblem you can work on. Or, if you already know the next subproblem, it’s often simpler with the first subproblem solved.

Many beginners try to solve the big problem in one go. Don’t do this . If the problem is sufficiently complex, you’ll get yourself tied in knots and make life a lot harder for yourself. Decomposing problems into smaller and easier to solve subproblems is a much better approach. Decomposition is the main way to deal with complexity, making problems easier and more approachable to solve and understand.

In short, break the big problem down and solve each of the smaller problems until you’ve solved the big problem.

Solving Fizz Buzz

To demonstrate this workflow in action, let’s solve Fizz Buzz

Understanding the problem

Write a program that takes a user’s input and prints the numbers from one to the number the user entered. However, for multiples of three print Fizz instead of the number and for the multiples of five print Buzz . For numbers which are multiples of both three and five print FizzBuzz .

This is the big picture problem we will be solving. But we can always make it clearer by rewording it.

Write a program that allows the user to enter a number, print each number between one and the number the user entered, but for numbers that divide by 3 without a remainder print Fizz instead. For numbers that divide by 5 without a remainder print Buzz and finally for numbers that divide by both 3 and 5 without a remainder print FizzBuzz .

Does your program have an interface? What will it look like? Our FizzBuzz solution will be a browser console program, so we don’t need an interface. The only user interaction will be allowing users to enter a number.

What inputs will your program have? Will the user enter data or will you get input from somewhere else? The user will enter a number from a prompt (popup box).

What’s the desired output? The desired output is a list of numbers from 1 to the number the user entered. But each number that is divisible by 3 will output Fizz , each number that is divisible by 5 will output Buzz and each number that is divisible by both 3 and 5 will output FizzBuzz .

Writing the pseudocode

What are the steps necessary to return the desired output? Here is an algorithm in pseudocode for this problem:

Dividing and conquering

As we can see from the algorithm we developed, the first subproblem we can solve is getting input from the user. So let’s start there and verify it works by printing the entered number.

With JavaScript, we’ll use the “prompt” method.

The above code should create a little popup box that asks the user for a number. The input we get back will be stored in our variable answer .

We wrapped the prompt call in a parseInt function so that a number is returned from the user’s input.

With that done, let’s move on to the next subproblem: “Loop from 1 to the entered number”. There are many ways to do this in JavaScript. One of the common ways - that you actually see in many other languages like Java, C++, and Ruby - is with the for loop :

If you haven’t seen this before and it looks strange, it’s actually straightforward. We declare a variable i and assign it 1: the initial value of the variable i in our loop. The second clause, i <= answer is our condition. We want to loop until i is greater than answer . The third clause, i++ , tells our loop to increment i by 1 every iteration. As a result, if the user inputs 10, this loop would print numbers 1 - 10 to the console.

Most of the time, programmers find themselves looping from 0. Due to the needs of our program, we’re starting from 1

With that working, let’s move on to the next problem: If the current number is divisible by 3, then print Fizz .

We are using the modulus operator ( % ) here to divide the current number by three. If you recall from a previous lesson, the modulus operator returns the remainder of a division. So if a remainder of 0 is returned from the division, it means the current number is divisible by 3.

After this change the program will now output this when you run it and the user inputs 10:

The program is starting to take shape. The final few subproblems should be easy to solve as the basic structure is in place and they are just different variations of the condition we’ve already got in place. Let’s tackle the next one: If the current number is divisible by 5 then print Buzz .

When you run the program now, you should see this output if the user inputs 10:

We have one more subproblem to solve to complete the program: If the current number is divisible by 3 and 5 then print FizzBuzz .

We’ve had to move the conditionals around a little to get it to work. The first condition now checks if i is divisible by 3 and 5 instead of checking if i is just divisible by 3. We’ve had to do this because if we kept it the way it was, it would run the first condition if (i % 3 === 0) , so that if i was divisible by 3, it would print Fizz and then move on to the next number in the iteration, even if i was divisible by 5 as well.

With the condition if (i % 3 === 0 && i % 5 === 0) coming first, we check that i is divisible by both 3 and 5 before moving on to check if it is divisible by 3 or 5 individually in the else if conditions.

The program is now complete! If you run it now you should get this output when the user inputs 20:

• Read How to Think Like a Programmer - Lessons in Problem Solving by Richard Reis.
• Watch How to Begin Thinking Like a Programmer by Coding Tech. It’s an hour long but packed full of information and definitely worth your time watching.
• Read this Pseudocode: What It Is and How to Write It article from Built In.

Knowledge check

The following questions are an opportunity to reflect on key topics in this lesson. If you can’t answer a question, click on it to review the material, but keep in mind you are not expected to memorize or master this knowledge.

• What are the three stages in the problem solving process?
• Why is it important to clearly understand the problem first?
• What can you do to help get a clearer understanding of the problem?
• What are some of the things you should do in the planning stage of the problem solving process?
• What is an algorithm?
• What is pseudocode?
• What are the advantages of breaking a problem down and solving the smaller problems?

Additional resources

This section contains helpful links to related content. It isn’t required, so consider it supplemental.

• Read the first chapter in Think Like a Programmer: An Introduction to Creative Problem Solving ( not free ). This book’s examples are in C++, but you will understand everything since the main idea of the book is to teach programmers to better solve problems. It’s an amazing book and worth every penny. It will make you a better programmer.
• Watch this video on repetitive programming techniques .
• Watch Jonathan Blow on solving hard problems where he gives sage advice on how to approach problem solving in software projects.

Support us!

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UNIT 1: How to Think Like an Engineer

Learning objectives.

• Explain what we mean by “Computational Thinking”.
• Describe the problem being solved in a computational algorithm.
• Explain the process for generating computational algorithms.
• Generate and test algorithms to solve computational problems.
• Evaluate computational algorithms for exactness, correctness, termination, generalizability and understandability.
• Explain the role of programming in the field of Informatics.

Introduction

The goal of this book is to teach you to solve computational problems and to think like an engineer. Computational problems are problems that can be solved by the use of computations (a computation is what you do when you calculate something). Engineers are people who solve problems – they invent, design, analyze, build and test “things” to fulfill objectives and requirements. The single most important skill for you to learn is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills.

This book strives to prepare you to write well-designed computer programs that solve interesting problems involving data.

Computational Thinking

Figure 1: “The seven components to computational thinking”(www.ignitemyfutureinschool.org/about)

Computational Thinking is the thought processes involved in understanding a problem and expressing its solution in a way that a computer can effectively carry out. Computational thinking involves solving problems, designing systems, and understanding human behavior (e.g. what the user needs or wants) – thinking like an engineer. Computational thinking is a fundamental skill for everyone, not just for programmers because computational thinking is what comes before any computing technology. [1]

Computer science is the study of computation — what can be computed and how to compute it whereas computational thinking is:

Conceptualizing , not programming. Computer science is not only computer programming. Thinking like a computer scientist means more than being able to program a computer. It requires thinking at multiple levels of abstraction;

Fundamental , not rote skill. A fundamental skill is something every human being must know to function in modern society. Rote means a mechanical routine;

A way that humans, not computers, think . Computational thinking is a way humans solve problems; it is not trying to get humans to think like computers. Computers are dull and boring; humans are clever and imaginative. We humans make computers exciting. Equipped with computing devices, we use our cleverness to tackle problems we would not dare take on before the age of computing and build systems with functionality limited only by our imaginations;

Complements and combines mathematical and engineering thinking . Computer science inherently draws on mathematical thinking, given that, like all sciences, its formal foundations rest on mathematics. Computer science inherently draws on engineering thinking, given that we build systems that interact with the real world;

Ideas , not artifacts. It’s not just the software and hardware artifacts we produce that will be physically present everywhere and touch our lives all the time, it will be the computational concepts we use to approach and solve problems, manage our daily lives, and communicate and interact with other people;

For everyone, everywhere . Computational thinking will be a reality when it is so integral to human endeavors it disappears as an explicit philosophy. [2]

Figure 2 “Are you happy?” by Typcut http://www.typcut.com/headup/are-you-happy

An algorithm specifies a series of steps that perform a particular computation or task. Throughout this book we’ll examine a number of different algorithms to solve a variety of computational problems.

Algorithms resemble recipes. Recipes tell you how to accomplish a task by performing a number of steps. For example, to bake a cake the steps are: preheat the oven; mix flour, sugar, and eggs thoroughly; pour into a baking pan; set the timer and bake until done.

However, “algorithm” is a technical term with a more specific meaning than “recipe”, and calling something an algorithm means that the following properties are all true:

• An algorithm is an unambiguous description that makes clear what has to be implemented in order to solve the problem. In a recipe, a step such as “Bake until done” is ambiguous because it doesn’t explain what “done” means. A more explicit description such as “Bake until the cheese begins to bubble” is better. In a computational algorithm, a step such as “Choose a large number” is vague: what is large? 1 million, 1 billion, or 100? Does the number have to be different each time, or can the same number be used again?
• An algorithm expects a defined set of inputs. For example, it might require two numbers where both numbers are greater than zero. Or it might require a word, or a list customer names.
• An algorithm produces a defined set of outputs. It might output the larger of the two numbers, an all-uppercase version of a word, or a sorted version of the list of names.
• An algorithm is guaranteed to terminate and produce a result, always stopping after a finite time. If an algorithm could potentially run forever, it wouldn’t be very useful because you might never get an answer.
• Must be general for any input it is given. Algorithms solve general problems (determine if a password is valid); they are of little use if they only solve a specific problem (determine if ‘comp15’ is a valid password)
• It is at the right level of detail…..the person or device executing the instruction know how to accomplish the instruction without any extra information.

Once we know it’s possible to solve a problem with an algorithm, a natural question is whether the algorithm is the best possible one. Can the problem be solved more quickly or efficiently?

The first thing you need to do before designing an algorithm is to understand completely the problem given. Read the problem’s description carefully, then read it again. Try sketching out by hand some examples of how the problem can be solved. Finally consider any special cases and design your algorithm to address them.

An algorithm does not solve a problem rather it gives you a series of steps that, if executed correctly, will result in a solution to a problem.

An Example Algorithm

Let us look at a very simple algorithm called find_max.

Problem : Given a list of positive numbers, return the largest number on the list.

Inputs : A list of positive numbers. This list must contain at least one number. (Asking for the largest number in a list of no numbers is not a meaningful question.)

Outputs : A number, which will be the largest number in the list.

Algorithm :

• Accept a list of positive numbers; set to nums_list
• Set max_number to 0.
• If the number is larger, set max_number to the larger number.
• max_number is now set to the largest number in the list of positive numbers, nums_list.

Does this meet the criteria for being an algorithm?

• Is it unambiguous? Yes. Each step of the algorithm consists of uncomplicated operations, and translating each step into programming code is straight forward.
• Does it have defined inputs and outputs? Yes.
• Is it guaranteed to terminate? Yes. The list nums_list is of finite length, so after looking at every element of the list the algorithm will stop.
• Is it general for any input? Yes. A list of any set of positive numbers works.
• Does it produce the correct result? Yes. When tested, the results are what are expected

Figure 3: Example Algorithm

How do we know if an algorithm is unambiguous, correct, comes to an end, is general AND is at the right level of detail? We must test the algorithm. Testing means verifying that the algorithm does what we expect it to do. In our ‘bake a cake’ example we know our algorithm is ‘working’ if, in the end, we get something that looks, smells and tastes like a cake.

Verifying your Algorithm

Figure 3 “ Keyboard ” by Geralt is licensed under CC 2

Your first step should be to carefully read through EACH step of the algorithm to check for ambiguity and if there is any information missing. To ensure that the algorithm is correct, terminates and is general for any input we devise ‘test cases’ for the algorithm.

A test case is a set of inputs, conditions, and expected results developed for a particular computational problem to be solved. A test case is really just a question that you ask of the algorithm (e.g. if my list is the three numbers 2, 14, and 11 does the algorithm return the number 14?). The point of executing the test is to make sure the algorithm is correct, that it terminates and is general for any input.

Good (effective) test cases:

• are easy to understand and execute
• are created with the user in mind (what input mistakes will be made? what are the preconditions?)
• make no assumptions (you already know what it is supposed to do)
• consider the boundaries for a specified range of values.

Let us look at the example algorithm from the previous section. The input for the algorithm is ‘a list of positive numbers’. To make it easy to understand and execute keep the test lists short. The preconditions are that the list only contains numbers and these numbers must be positive so include a test with a ‘non-number’ (i.e. a special character or a letter) and a test with a negative number. The boundaries for the list are zero and the highest positive number so include a test with zero and a large positive number. That is it! Here is an example of three different test cases.

Manually, you should step through your algorithm using each of the three test cases, making sure that the algorithm does indeed terminate and that you get your expected result. As our algorithms and programs become more complex, skilled programmers often break each test case into individual steps of the algorithm/program and indicate what the expected result of each step should be. When you write a detailed test case, you don’t necessarily need to specify the expected result for each test step if the result is obvious.

In computer programming we accept a problem to solve and develop an algorithm that can serve as a general solution. Once we have such a solution, we can use our computer to automate the execution. Programming is a skill that allows a competent programmer to take an algorithm and represent it in a notation (a program) that can be followed by a computer. These programs are written in programming languages (such as Python). Writing a correct and valid algorithm to solve a computational problem is key to writing good code. Learn to Think First and coding will come naturally!

The Process of Computational Problem Solving

Computational problem solving does not simply involve the act of computer programming. It is a process, with programming being only one of the steps. Before a program is written, a design for the program must be developed (the algorithm). And before a design can be developed, the problem to be solved must be well understood. Once written, the program must be thoroughly tested. These steps are outlined in Figure 5.

Figure 5: Process of Computational Problem Solving [footnote]Dierbach, Charles. Introduction to Computer Science Using Python: A Computational Problem-solving Focus. Wiley Publishing, 2012, pp17-18.[/footnote]

Values and Variables

A value is one of the basic things computer programs works with, like a password or a number of errors.

Values belong to different types: 21 is an integer (like the number of errors), and ‘comp15’ is a string of characters (like the password). Python lets you give names to values giving us the ability to generalize our algorithms.

One of the most powerful features of a programming language is the ability to use variables. A variable is simply a name that refers to a value as shown below,

Whenever the variable errors appears in a calculation the current value of the variable is used.

We need some way of storing information (i.e. the number of errors or the password) and manipulate them as well. This is where variables come into the picture. Variables are exactly what the name implies – their value can vary, i.e., you can store anything using a variable. Variables are just parts of your computer’s memory where you store some information. Unlike literal constants, you need some method of accessing these variables and hence you give them names.

Programmers generally choose names for their variables that are meaningful and document what the variable is used for. It is a good idea to begin variable names with a lowercase letter . The underscore character (_) can appear in a name and is often used in names with multiple words.

A program is a sequence of instructions that specifies how to perform a computation. The computation might be something mathematical, such as solving a system of mathematical equations or finding the roots of a polynomial, but it can also be a symbolic computation, such as searching and replacing text in a document or something graphical, like processing user input on an ATM device.

What is a Program?

Figure 6: “ Python Code ” by nyuhuhuu is licensed under CC-BY 2.0

The details look different in different computer programming languages, but there are some low-level conceptual patterns (constructs) that we use to write all programs. These constructs are not just for Python programs, they are a part of every programming language.

input Get data from the “outside world”. This might be reading data from a file, or even some kind of sensor like a microphone or GPS. In our initial algorithms and programs, our input will come from the user typing data on the keyboard.

output Display the results of the program on a screen or store them in a file or perhaps write them to a device like a speaker to play music or speak text.

sequential execution Perform statements one after another in the order they are encountered in the script.

conditional execution Checks for certain conditions and then executes or skips a sequence of statements.

repeated execution Perform some set of statements repeatedly, usually with some variation.

reuse Write a set of instructions once and give them a name and then reuse those instructions as needed throughout your program.

Believe it or not, that’s pretty much all there is to it. Every computer application you’ve ever used, no matter how complicated, is made up of constructs that look pretty much like these. So you can think of programming as the process of breaking a large, complex task into smaller and smaller subtasks until the subtasks are simple enough to be performed with one of these basic constructs. The “art” of writing a program is composing and weaving these basic elements together many times over to produce something that is useful to its users.

Computational Problem Design Using the Basic Programming Constructs

The key to better algorithm design and thus to programming lies in limiting the control structure to only three constructs as shown below.

• The Sequence structure (sequential execution)
• The Decision, Selection or Control structure (conditional execution)
• Repetition or Iteration Structure (repeated execution)

Figure 7: the 3 Programming Constructs

  Let us look at some examples for the sequential control and the selection control.

Sequential Control Example

The following algorithm is an example of sequential control .

Problem : Given two numbers, return the sum and the product of the two numbers.

Inputs : Two numbers.

Outputs : The sum and the product.

• display “Input two numbers”
• sum = number1 + number2
• print “The sum is “, sum
• product = number1 * number2
• print “The product is “, product
• Is it guaranteed to terminate? Yes. Sequential control, by its nature, always ends.
• Is it general for any input? Yes. Any two numbers work in this design.
• Does it produce the correct result? Yes. When tested, the results are what are expected.

Here is an example of three different test cases that are used to verify the algorithm.

Selection Control

The following two algorithms are examples of selection control which uses the ‘IF’ statement in most programming languages.

Problem : Given two numbers, the user chooses to either multiply, add or subtract the two numbers. Return the value of the chosen calculation.

Inputs : Two numbers and calculation option.

Outputs : The value of the chosen calculation.

The relational (or comparison) operators used in selection control are:

= is equal to

> is greater than

< is less than

>= is greater than or equal

<= is less than or equal

<> is not equal to

• display “choose one of the following”
• display “m for multiply”
• display “a for add”
• display “s for subtract”
• accept choice
• display “input two numbers you want to use”
• accept number1, number2
• if choice = m then answer= number1 * number2
• if choice = a then answer= number1 + number2
• if choice = s then answer= number1 -number212. if choice is not m, a, or s then answer is NONE
• display answer
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s choice and the two numbers the algorithm will stop.
• Is it general for any input? Yes. Any two numbers work in this design and only a choice of a’m’, ‘a’, or ‘s’ will result in numeric output.

This example uses an extension of the simple selection control structure we just saw and is referred to as the ‘IF-ELSE’ structure.

Problem : Accept from the user a positive integer value representing a salary amount, return tax due based on the salary amount.

Inputs : One positive integer number.

Outputs : The calculated tax amount.

• accept salary
• If salary < 50000 then
• Tax = 0 Else
• If salary > 50000 AND salary < 100000 then
• Tax = 50000 * 0.05 Else
• Tax = 100000 * 0.30
• display Tax
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s number, even if it is negative, the algorithm will stop.
• Is it general for any input? Yes. Any number entered in this design will work.

Iterative Control Examples

The third programming control is the iterative or, also referred to as, the repetition structure. This control structure causes certain steps to be repeated in a sequence a specified number of times or until a condition is met. This is what is called a ‘loop’ in programming

In all programming languages there are generally two options: an indefinite loop (the Python ‘WHILE’ programming statement) and a definite loop (the Python ‘FOR’ programming statement). We can use these two constructs, WHILE and FOR, for iterations or loops in our algorithms.

Note for Reader: A definite loop is where we know exactly the number of times the loop’s body will be executed. Definite iteration is usually best coded as a Python for loop. An indefinite loop is where we do not know before entering the body of the loop the exact number of iterations the loop will perform. The loop just keeps going until some condition is met. A while statement is used in this case.

The following algorithm is an example of iterative control using WHILE .

Problem : Print each keyboard character the users types in until the user chooses the ‘q’ (for ‘quit’) character.

Inputs : A series of individual characters.

Outputs : Each character typed in by the user.

• initialize (set) letter = ‘a’
• WHILE letter <> ‘q’
• ACCEPT letter
• DISPLAY “The character you typed is”, letter
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s keyboard character, even if it is not a letter, the algorithm will stop.
• Is it general for any input? Yes. Any keyboard character entered in this design will work.

The following algorithm is an example of iterative control using FOR . This statement is used when the number of iterations is known in advance.

Problem : Ask the user how many words they want to enter then print the words entered by the user.

Inputs : Number of words to be entered; this value must be a positive integer greater than zero. Individual words.

Outputs : Each word typed in by the user.

• accept num_words (must be at least one)
• repeat num_words times (FOR 1 to num_words)
• accept word
• DISPLAY “The word you entered is”, word
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s number of words to enter and any characters typed on the keyboard, even if it is not a ‘word’ per say, the algorithm will stop.
• Is it general for any input? Yes. Any positive integer greater than zero and any size ‘word’ will work.

Here is an example of two different test cases that are used to verify the algorithm.

The Role of Programming in the Field of Informatics

Figure8: iPhone apps by Jaap Arriens/NurPhoto via Getty Images (abcnews.go.com)

You see computer programming in use every day. When you use Google or your smartphone, or watch a movie with special effects, there is programing at work. When you order a product over the Internet, there is code in the web site, in the cryptography used to keep your credit card number secure, and in the way that UPS routes their delivery vehicle to get your order to you as quickly as possible.

Programming is indeed important to an informatics professional as they are interested in finding solutions for a wide variety of computational problems involving data.

When you Google the words “pie recipe,” Google reports that it finds approximately 38 million pages, ranked in order of estimated relevance and usefulness. Facebook has approximately 1 billion active users who generate over 3 billion comments and “Likes” each day. GenBank, a national database of DNA sequences used by biologists and medical researchers studying genetic diseases, has over 100 million genetic sequences with over 100 billion DNA base pairs. According to the International Data Corporation, by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.

Figure 9: The Digital Universe ( www.emc.com/leadership/digital-universe/2014iview/images )

  Doing meaningful things with data is challenging, even if we’re not dealing with millions or billions of things. In this book, we will be working with smaller sets of data. But much of what we’ll do will be applicable to very large amounts of data too.

Unit Summary

Computational Thinking is the thought processes involved in formulating a problem and expressing its solution in a way that a computer—human or machine—can effectively carry out.

Computational Thinking is what comes before any computing technology—thought of by a human, knowing full well the power of automation.

Writing a correct and valid algorithm to solve a computational problem is key to writing good code.

• What are the inputs?
• What are the outputs (or results)?
• Can we break the problem into parts?
• Think about the connections between the input & output.
• Consider designing ‘backwards’.
• Have you seen the problem before? In a slightly different form?
• Can you solve part of the problem?
• Did you use all the inputs?
• Can you test it on a variety of inputs?
• Can you think of how you might write the algorithm differently if you had to start again?
• Does it solve the problem? Does it meet all the requirements? Is the output correct?
• Does it terminate?
• Is it general for all cases?

Practice Problems

• Write about a process in your life (e.g. driving to the mall, walking to class, etc.) and estimate the number of steps necessary to complete the task. Would you consider this a complex or simple task? What happens if you scale that task (e.g. driving two states away to the mall)? Is your method the most efficient? Can you come up with a more efficient way?

• Write an algorithm to find the average of 25 test grades out of a possible 100 points.
• If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle: “If any of the three lengths is greater than the sum of the other two, then you cannot form a triangle. Otherwise, you can.”Write an algorithm that accepts three integers as arguments, and that displays either “Yes” or “No,” depending on whether you can or cannot form a triangle from sticks with the given lengths.
• ROT13 is a weak form of encryption that involves “rotating” each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so ‘A’ shifted by 3 is ‘D’ and ‘Z’ shifted by 1 is ‘A’. Write an algorithm that accepts a word and an integer from the user, and that prints a new encrypted word that contains the letters from the original word “rotated” by the given amount (the integer input). For example, “cheer” rotated by 7 is “jolly” and “melon” rotated by −10 is “cubed.”
• Write an algorithm which repeatedly accepts numbers until the user enters “done”. Once “done” is entered, display the total sum of all the numbers, the count of numbers entered, and the average of all the numbers.
• Write an algorithm that sums a series of ten positive integers entered by the user excluding all numbers greater than 100. Display the final sum.
• Wing, Jeannette M. "Computational thinking." Communications of the ACM 49.3 (2006): 33-35. ↵

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• Number Theory
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• Everyday Math
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Computer Science

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Take a guided, problem-solving based approach to learning Computer Science. These compilations provide unique perspectives and applications you won't find anywhere else.

Computer Science Fundamentals

What's inside.

• Tools of Computer Science
• Computational Problem Solving
• Algorithmic Thinking

Algorithm Fundamentals

• Building Blocks
• Array Algorithms
• The Speed of Algorithms
• Stable Matching

Programming with Python

• Introduction
• String Manipulation
• Loops, Functions and Arguments

Community Wiki

Browse through thousands of Computer Science wikis written by our community of experts.

Types and Data Structures

• Abstract Data Types
• Array (ADT)
• Double Ended Queues
• Associative Arrays
• Priority Queues
• Array (Data Structure)
• Disjoint-set Data Structure (Union-Find)
• Dynamic Array
• Linked List
• Unrolled Linked List
• Hash Tables
• Bloom Filter
• Cuckoo Filter
• Merkle Tree
• Recursive Backtracking
• Fenwick Tree
• Binary Search Trees
• Red-Black Tree
• Scapegoat Tree
• Binary Heap
• Binomial Heap
• Fibonacci Heap
• Pairing Heap
• Graph implementation and representation
• Adjacency Matrix
• Spanning Trees
• Social Networks
• Kruskal's Algorithm
• Regular Expressions
• Divide and Conquer
• Greedy Algorithms
• Randomized Algorithms
• Complexity Theory
• Big O Notation
• Master Theorem
• Amortized Analysis
• Complexity Classes
• P versus NP
• Dynamic Programming
• Backpack Problem
• Egg Dropping
• Fast Fibonacci Transform
• Karatsuba Algorithm
• Sorting Algorithms
• Insertion Sort
• Bubble Sort
• Counting Sort
• Median-finding Algorithm
• Binary Search
• Depth-First Search (DFS)
• Breadth-First Search (BFS)
• Shortest Path Algorithms
• Dijkstra's Shortest Path Algorithm
• Bellman-Ford Algorithm
• Floyd-Warshall Algorithm
• Johnson's Algorithm
• Matching (Graph Theory)
• Matching Algorithms (Graph Theory)
• Flow Network
• Max-flow Min-cut Algorithm
• Ford-Fulkerson Algorithm
• Edmonds-Karp Algorithm
• Shunting Yard Algorithm
• Rabin-Karp Algorithm
• Knuth-Morris-Pratt Algorithm
• Basic Shapes, Polygons, Trigonometry
• Convex Hull
• Finite State Machines
• Turing Machines
• Halting Problem
• Kolmogorov Complexity
• Traveling Salesperson Problem
• Pushdown Automata
• Regular Languages
• Context Free Grammars
• Context Free Languages
• Signals and Systems
• Linear Time Invariant Systems
• Predicting System Behavior

Programming Languages

• Subroutines
• List comprehension
• Primality Testing
• Pattern matching
• Logic Gates
• Control Flow Statements
• Object-Oriented Programming
• Classes (OOP)
• Methods (OOP)

Cryptography and Simulations

• Caesar Cipher
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• RSA Encryption
• Enigma Machine
• Diffie-Hellman
• Knapsack Cryptosystem
• Secure Hash Algorithms
• Entropy (Information Theory)
• Huffman Code
• Error correcting codes
• Symmetric Ciphers
• Inverse Transform Sampling
• Monte-Carlo Simulation
• Genetic Algorithms
• Programming Blackjack
• Machine Learning
• Supervised Learning
• Unsupervised Learning
• Feature Vector
• Naive Bayes Classifier
• K-nearest Neighbors
• Support Vector Machines
• Principal Component Analysis
• Ridge Regression
• k-Means Clustering
• Markov Chains
• Hidden Markov Models
• Gaussian Mixture Model
• Collaborative Filtering
• Artificial Neural Network
• Feedforward Neural Networks
• Backpropagation
• Recurrent Neural Network

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What is computer programming, exactly? A techie's guide

The 21st-century world runs on computers. And computers run on programs. Understanding computer programming unlocks the power of computing systems -- and programming opens career doors.

Computer programs communicate information to computing devices. Computers then carry out tasks based on the program instructions. Simple programs tell computers to run calculations, while complex programs can run video games, analyze big data, or drive a car.

Our guide defines computer programs, explores uses of programming, and looks at the knowledge and skills required for careers in programming. 

What is computer programming?

What is computer programming? Programming, also known as coding, refers to the process of writing instructions for computing devices and systems. A computer program translates those instructions into a language that computers can understand.  

Computer programmers use many different languages to command computers. Popular programming languages include Python, JavaScript, Java, and the C-languages. 

The tech industry relies on computer programming to create innovative new uses for computers. Groundbreaking fields like machine learning and artificial intelligence depend on computer programming.

Computer users interact with programs frequently. Web browsers, for example, are specialized computer programs. These user-facing programs fall into the category of front-end development . In contrast, back-end development creates programs for tasks the user does not see, including server communication. 

How is computer programming used?

Computer programmers created every application that computers run -- from photo editing software to word processors and web browsers. Programming languages unlock the power of computing systems. And without computer programming, our computing devices would not function. 

In addition to the uses of programming visible to users, programming languages also manage the hidden side of computing. Programs pull information from databases, implement security procedures to protect private data, and operate memory backup systems.

What computer programmers do

Computer programmers write code in languages like JavaScript, Python , and C++. Depending on their focus area -- web development, mobile application development, software engineering, and so on -- they use different languages. 

Computer programmers need more than fluency in one or more programming languages. They also need to know how to debug and modify code. Programmers often work in teams. 

The process of creating a program is complex and involves steps such as:

Conceptualizing the goal of the application

Building a layout of the different parts of the program

Writing code

Debugging the code and resolving any errors

Testing the application 

Releasing the program to beta users

Computer programming jobs

Many tech jobs require programming skills. For example, computer programmers , web developers , software developers , and software engineers all use coding skills regularly. 

According to the Bureau of Labor Statistics , the median annual wage for computer and information technology occupations was $91,250 in May 2020, more than twice the $41,950 median annual wage for all occupations. The best computer science jobs also show growth above the national average.

Other computer programming jobs include:

Database administrator

Computer systems analyst

Information security analyst

Data scientist

Network architect

Becoming a computer programmer

Computer programmers gain coding skills through college programs, coding bootcamps , and self-study. A degree in computer science or programming builds strong coding skills. If you're wondering how to become a software engineer , researching training options is a good place to start.

Students can also enroll in online courses, certificate programs, or bootcamps focused on particular programming languages.

Prospective programmers should consider their strengths and career goals when choosing languages to learn. The easiest programming languages have simple syntax and can provide an entry point for tackling more complex languages. Note that different career paths require different programming languages.

What is computer programming used for?

Programming languages tell computing systems to perform tasks. Programmers code software, hardware, and other applications that allow people to use computers. 

Is computer programming hard?

Learning a programming language requires attention to detail and strong problem-solving skills. Focusing on an easier programming language helps new learners master core programming skills.

What language is used for computer programming?

Computer programmers use many different languages, including JavaScript, Python, C++, and Java. Tech professionals use different languages depending on their goals and focus areas.

This article was reviewed by Monali Mirel Chuatico

In 2019, Monali Mirel Chuatico graduated with her bachelor's in computer science, which gave her the foundation that she needed to excel in roles such as a data engineer, front-end developer, UX designer, and computer science instructor. Monali is currently a data engineer at Mission Lane. As a data analytics captain at a nonprofit called COOP Careers, Monali helps new grads and young professionals overcome underemployment by teaching them data analytics tools and mentoring them on their professional development journey. Monali is passionate about implementing creative solutions, building community, advocating for mental health, empowering women, and educating youth.  Monali Mirel Chuatico is a paid member of the Red Ventures Education freelance review network.  

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Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

• Post author By bicycle-u
• Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

• Mastering programming languages and algorithms
• Staying updated with technological advancements in the field
• Practicing problem solving through coding challenges and projects
• Seeking feedback and learning from mistakes
• Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

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Problem Solving Using Computer (Steps)

Computer based problem solving is a systematic process of designing, implementing and using programming tools during the problem solving stage. This method enables the computer system to be more intuitive with human logic than machine logic. Final outcome of this process is software tools which is dedicated to solve the problem under consideration. Software is just a collection of computer programs and programs are a set of instructions which guides computer’s hardware. These instructions need to be well specified for solving the problem. After its creation, the software should be error free and well documented. Software development is the process of creating such software, which satisfies end user’s requirements and needs.

The following six steps must be followed to solve a problem using computer.

• Problem Analysis
• Program Design - Algorithm, Flowchart and Pseudocode
• Compilation and Execution
• Debugging and Testing
• Program Documentation

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Marking a Milestone: Four Years of Daily Study Groups

From data to discovery: studying computational biology with wolfram, navigating quantum computing: accelerating next-generation innovation, unlock innovative problem-solving skills with creative computation.

As computers continue to perform an increasing number of tasks for us, it’s never been more important to learn how to use computers in creative ways. Creative computing, an interdisciplinary subject combining coding with artistic expression, allows us to blend technology with human experiences. Learning to create in this way can help you unlock your innovative problem-solving skills. By mastering creative computation, you can create interactive artwork, design immersive experiences and develop creative solutions to real-world challenges.

Wolfram U ’s new Creative Computation course combines an introduction to Wolfram Language coding with a project-based exploration of various art forms, like visual art, poetry, audio and video game design. If you’ve never coded in Wolfram Language before, this course is a fantastic introduction to applied computing and will help you learn the language for any project. If you’ve already mastered the basics of coding, this course will help you apply your skills to fascinating new problems and projects.

We would love for you to join us in this interactive course as we explore what it means to work creatively with coding.

Motivation from History

Creative computing is a relatively new subject, but people have been using technology to make art for centuries. From the loom to the printing press or Walkman to Atari, technology has been part of art for as long as both have existed.

We now have a variety of exciting and creative ways to engage with computers, from AI-generated images to immersive virtual realities.

In this course, you will learn how to use Wolfram Language to create various forms of art. There are four main sections to the course: Computational Art, Computational Strings, Sound and Game Development. In each section, there are lessons teaching Wolfram Language skills, with associated exercises, and at the end of each section, there is a larger project. The projects are designed for you to stretch your creative muscles and use your new coding skills to create art. You’ll learn how to create visual art using images, how to write poetry using string manipulation, how to visualize audio and how to make text-based and graphics-based video games, all while learning how to code in Wolfram Language.

Here is a sneak peek at some of the topics in the course (shown in the left-hand column):

With 16 lessons, five quizzes and four projects, this course should take around five hours to complete. We recommend doing all the activities and projects to maximize your understanding and explore your new skills.

There is no background required to participate in this course. We will teach you all the coding skills you need to make the projects, so all that is required is your excitement and creativity.

Let’s explore what’s in the course.

There are 16 lessons in this course spread out over the five total sections (Computational Thinking and Coding, Computational Art, Computational Strings, Sound and Game Development). In each lesson, you will explore a different aspect of coding through a short video. You’ll start off by exploring the concept of computational thinking: how to translate your thoughts and your creativity into something the computer can understand and how to work with a computer to build creative artifacts. Here is a short excerpt from the video for this lesson:

Each lesson teaches a specific coding skill, with lots of examples and exploration of key concepts. In the Computational Art section, the goal is to use images and graphics to create a piece of art. In order to do that, we need to learn skills like variables, functions, lists, the Table and Map functions, colors, graphics and randomness, and image manipulation. Each skill is taught with an interactive video lesson in conjunction with exercises, before you use the project to test your knowledge.

The video lessons range from 5–13 minutes in length, and each video is accompanied by a transcript notebook displayed on the right-hand side of the screen. You can copy and paste Wolfram Language input directly from the transcript notebook to the embedded scratch notebook to try the examples for yourself.

Each lesson has a set of exercises to review the concepts covered during the lesson. Since this course is designed for independent study, a detailed solution is given for all exercises. Each exercise will help you practice a specific skill you’ve learned so that you are ready to use that skill in the project. Here is an example of an exercise from lesson 6 on image manipulation:

The exercise notebooks are interactive, so you can try variations of each problem in the Wolfram Cloud . You’re encouraged to blend skills together as you learn them. For example, for the aforementioned exercise, you could use the skills you just learned about randomness to replace the dominant colors in the image of the wolf with random colors, or you could import images to do the same exercise with a different image. When you’ve gotten further in the course, you could come back and build your own function that can do this to any two images.

Each section of the course includes a short project, and the Game Development section has two longer projects. In each case, you’ll use the skills you learned in that section to build something creative. In the first three sections, we provide detailed solutions and walk you though our processes, but in the Game Development section, we encourage you to build something unique.

In the Computational Art section, you’ll make art using images and shapes. In Computational Strings, you’ll write a Mad Libs haiku. In Sound, you’ll make an audio visualizer. In Game Development, you’ll make a text adventure game and a graphics-based Pac-Man –style game.

These projects will allow you to celebrate your successes and practice your new coding skills while cementing your understanding of creative computation.

Each section of the course ends with a short quiz, which allows you to demonstrate your understanding:

You will get instant feedback on your solutions, and you’re encouraged to try out the code.

Course Certificate

You are encouraged to watch all the lessons and attempt the projects and quizzes in the recommended sequence, since each topic in the course relies on earlier concepts and techniques. When you watch all 16 lesson videos and pass the five course quizzes, you will earn a certificate of course completion. The Track My Progress status bar in the course helps you to chart your progress, showing you where you left off from your previous course session. While you don’t have to submit projects to earn a certificate, they are a fundamental part of gaining computational skills, and we look forward to connecting with course users about their projects on Wolfram Community . Your course certificate represents completion of the basic course requirements, demonstrates your interest in exploring the latest technology and in building new computational skills, and it will add value to your resume or social media profile.

You are also encouraged to use the skills you learn in this course to go on to earn Level 1 certification for Wolfram Language proficiency . While the course does not require the same level of mathematics as the Level 1 certification exam, it will prepare you well for accomplishing the range of computational tasks that are required for Level 1 certification.

A Building Block for Success

A mastery of the fundamental concepts of creative computing will prepare you for working with computers to innovatively solve problems. Whether you’re interested in creating art or you’re interested in developing your coding skills, this course will provide a detailed foundation in both. Learning Wolfram Language is a valuable pursuit regardless of your career aspirations, as you can use the skills you learn in this course in any field.

Acknowledgements

I would like to thank my coauthor Eryn Gillam for their major contributions to the development of this course, as well as others who helped this course come together, including (but not limited to) Anisha Basil, Abrita Chakravarty, Cassidy Hinkle, Joyce Tracewell, Arben Kalziqi, Isabel Skidmore, Zach Shelton, Simeon Buttery, Ryan Domier and Eder Ordonez.

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Related Posts

Computer software can help solve the overnight truck parking problem

State governments should examine and implement cost-effective technologies that could help truckers find the best places to park..

The long-distance trucking industry faces several significant problems, and finding safe overnight truck parking spaces has remained one of the most challenging. Given the vital role of trucking in the U.S. economy, making it easier for truckers to locate open parking spots should be a top priority for trucking companies and state transportation departments. As technology rapidly develops, computer vision software coupled with live camera feeds of parking facilities can help truckers with this struggle.

The use of trucks to move freight in the U.S. is growing and saw a 27% increase in ton-miles moved from 2021 to 2022, the latest data available from the Bureau of Transportation. According to the Federal Motor Carrier Safety Administration, 3.36 million combination trucks are registered in the United States. However, in U.S. public rest areas, there are only 40,000 truck parking spaces .

Improving access to truck parking space information would help alleviate some of the system-wide stress. Truckers often rely on third-party apps to plot their routes and to find available rest areas during long hauls. One popular app, Trucker Path , accomplishes this using crowdsourced data similar to popular navigation apps like Waze. The more users, the more accurate the data. Trucker Path receives updates from “ [600,000] truckers ” on parking availability to help other truckers plan their routes. The app also estimates the likelihood of open spaces based on parking history at a given location.

While this service is helpful, there’s room for an alternative that receives data directly from the source—a live feed displaying parking availability that doesn’t depend on truckers using a particular app. Some states have conducted pilot programs on parking information feeds, but in 2017, the Kansas Department of Transportation (KDOT) tested combining computer vision with camera feeds.

In 2017, KDOT began work on a truck-parking information management system and started monitoring 160 truck parking spaces at 16 public sites. KDOT’s proposal used camera poles installed near the truck parking areas to create a 3D model of the parking area and send updates to signs along the Interstate, showing parking availability for the next two to three rest areas ahead. 

Given recent technological advancements, this system could be further improved. Pattern recognition software can be trained to detect parking spot openings with only one camera, so long as the parking spots are in view. There’s less of a need for a 3D model of the parking area itself, and camera images have increased in resolution since 2017. Additionally, in rest areas where cameras already cover much of the designated truck parking, implementation would be cheaper.

Currently, parking availability is tracked in several ways, including parking garages, rest areas, and other facilities. Conventional parking availability systems use schedules, crowdsourcing, or in-ground sensors installed at each parking space to determine whether a spot is occupied. The latter method of in-ground sensors often comes with high costs for both installation and maintenance.

In contrast, software can incorporate multiple data sources from in-ground sensors, camera feeds, schedules, and so on to paint a more comprehensive picture of parking availability. Computer vision object-detection algorithms can analyze camera feeds to detect whether a space is being used. Given the relative costs of in-ground sensors and high-resolution cameras, leveraging computer vision and cameras can greatly reduce costs compared to traditional in-ground parking availability systems. 

Many parking facilities already have security cameras that could be leveraged by computer vision systems, reducing initial setup costs. And those that don’t could install them, improving security and parking management. 

Figure 1 provides a visual example of how such a system would operate. Cameras detect a car’s license plate upon entry into the lot and as it moves through the garage. When the car parks in a space, the “condition” of the parking spot is changed from zero to one, indicating that the spot is filled by the identified car, in a simpler system focused strictly on availability, the license plate number would be irrelevant and could be cut.

Figure 1: Parking Detection Example

Source: In Hwan Jung et al., “Smart Parking Management System Using AI,” Webology 19 (2022). https://www.webology.org/data-cms/articles/20220123012239pmWEB19307.pdf (24 Apr. 2024).

The data could be fed to roadside LED displays outside rest areas or other truck parking locations. It could also be made freely available through a public application programming interface (API) so that any driver app could fetch real-time parking occupancy data. 

Since state transportation departments run rest areas, this approach could be implemented via a pilot program at the state level, similar to KDOT’s approach in 2017. Improved parking information would enable better allocations of scarce parking spaces and reduce time wasted by drivers searching for parking. This would enhance the safety of trucks and the efficiency of their freight movements.

If a program like this is successful and lowers costs, other states could work to emulate its success as a promising alternative to other existing methods of parking availability tracking.

Computer vision software coupled with live camera feeds of parking facilities is a promising alternative to conventional static parking availability signs. State governments should examine and implement cost-effective technologies that could help truckers find the best places to park.

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Object-Oriented Programing(OOP) Concepts for Designing Sytems

Object-oriented programming, or OOP, is like a set of tools that helps designers and developers create software systems. Think of it as a way to organize and build digital worlds piece by piece. In simpler words, OOP is a way of thinking and designing computer programs that mimic how we organize things in the real world. In this article, we’ll explore the core concepts of OOPs things like encapsulation, inheritance, polymorphism, and more—and see how they help us design better software systems.

Important Topics for OOPS for Designing Systems

What is Object-Oriented Programming?

Importance and benefits of object-oriented programming (oop) in system design, classes and objects in object-oriented programing(oop), inheritance in object-oriented programing(oop), polymorphism in object-oriented programing(oop), abstraction in object-oriented programing(oop), encapsulation in object-oriented programing(oop), relationships between classes in oops for designing systems, solid principles in oops for designing systems, real-world examples of oops in system design.

Object-Oriented Programming (OOP) in system design is a paradigm that organizes software design around objects and data rather than actions and logic. In this paradigm, objects represent real-world entities, encapsulating both data (attributes or properties) and behavior (methods or functions).

Object-oriented programming (OOP) is crucial in system design for several reasons, offering a range of benefits that contribute to the efficiency, maintainability, and scalability of software systems:

• OOP facilitates the modular design of systems by breaking them down into smaller, self-contained units called objects.
• Each object encapsulates its data and functionality, hiding internal details from other parts of the system.
• This encapsulation enhances code organization, making it easier to understand, maintain, and modify individual components without impacting the rest of the system.
• OOP promotes code reusability through mechanisms such as inheritance and composition.
• Inheritance allows new classes to inherit properties and behaviors from existing classes, enabling developers to reuse code and build upon existing functionality.
• Composition enables objects to contain instances of other objects, further enhancing code reuse and promoting a modular design approach.
• OOP encourages abstraction, allowing developers to represent real-world entities and concepts in a simplified and generalized manner.
• By focusing on essential characteristics and hiding implementation details, abstraction enhances system comprehensibility and maintainability.
• Abstraction also facilitates the identification of common patterns and the creation of reusable design frameworks and libraries.
• Inheritance and polymorphism are key features of OOP that enable the creation of hierarchical class structures and promote code extensibility and flexibility.
• Inheritance allows classes to be organized into parent-child relationships, with child classes inheriting properties and behaviors from their parent classes.
• Polymorphism enables objects to be treated uniformly through a common interface, allowing for flexible and interchangeable implementations.
• OOP simplifies the process of software maintenance and testing by providing a clear and structured design framework.
• Encapsulation limits the scope of changes required when modifying or extending functionality, reducing the risk of unintended side effects.
• OOP supports the development of scalable software systems by enabling teams to work collaboratively on large-scale projects.
• The modular design encourages code reuse and parallel development, allowing teams to focus on developing and maintaining smaller, manageable components.

Overall, Object-Oriented Programming plays a vital role in system design by providing a flexible, modular, and maintainable approach to software development. By leveraging the benefits of modularity, encapsulation, abstraction, inheritance, polymorphism, and code reusability, OOP helps developers create robust, scalable, and adaptable software systems

In Object-Oriented Programming (OOP), classes and objects are fundamental concepts used in system design to model real-world entities and their interactions. Here’s an overview of classes and objects in OOP system design:

• Definition : A class is a blueprint or template for creating objects. It defines the properties (attributes) and behaviors (methods) that objects of that class will possess. Think of a class as a cookie cutter that defines the shape and characteristics of cookies you can make.
• Attributes : Attributes are the data members or variables associated with a class. They represent the state of an object and describe its properties. For example, in a “Car” class, attributes might include “make,” “model,” “color,” and “year.”
• Methods : Methods are the functions or procedures defined within a class that describe its behavior. They encapsulate the operations that can be performed on objects of the class. For example, in a “Car” class, methods might include “start_engine,” “accelerate,” “brake,” and “turn_off_engine.”
• Instantiation : An object is an instance of a class. It is created based on the blueprint defined by the class. Think of objects as actual cookies made using the cookie cutter (class). Each object has its own set of attributes and can perform actions defined by its class.
• Attributes : Objects have specific values for the attributes defined by their class. These values represent the state of the object. For example, an object of the “Car” class might have attributes like “make” = “Toyota,” “model” = “Camry,” “color” = “red,” and “year” = 2022.
• Methods : Objects can invoke methods defined by their class to perform various actions or operations. These methods operate on the object’s data (attributes) and can modify its state. For example, an object of the “Car” class can invoke methods like “start_engine,” “accelerate,” “brake,” and “turn_off_engine” to control its behavior.

Inheritance is a key concept in Object-Oriented Programming (OOP) that plays a crucial role in system design. It allows classes to inherit properties and behaviors from other classes, enabling the creation of hierarchical relationships and promoting code reuse. Here’s how inheritance is used in designing systems:

• Inheritance allows a class (subclass or derived class) to inherit attributes and methods from another class (superclass or base class). The subclass can extend or modify the functionality of the superclass while inheriting its common characteristics.
• In system design, inheritance helps in organizing classes into hierarchical structures based on their relationships. This hierarchical structure reflects the “is-a” relationship between classes. For example, a “Car” class might inherit from a more general “Vehicle” class, indicating that a car is a type of vehicle.
• One of the primary benefits of inheritance is code reuse. By inheriting from a superclass, a subclass can reuse the attributes and methods defined in the superclass without needing to redefine them. This promotes a modular design approach and reduces code duplication.

Polymorphism is another crucial concept in Object-Oriented Programming (OOP) that plays a significant role in system design. It allows objects of different classes to be treated as objects of a common superclass, enabling flexibility, extensibility, and code reuse. Here’s how polymorphism is used in designing systems:

• Polymorphism allows objects of different types to be treated uniformly through a common interface. It enables the same code to operate on different types of objects, providing flexibility and extensibility in system design.
• Polymorphism is often achieved through method overriding, where subclasses provide their own implementation of a method defined in their superclass. When a method is called on an object, the runtime environment determines which implementation to invoke based on the actual type of the object.
• Polymorphism can also be achieved through interfaces or abstract classes, where multiple classes implement the same interface or extend the same abstract class. This allows objects to be treated uniformly based on their common interface or superclass, regardless of their specific implementations.

Abstraction is a fundamental concept in Object-Oriented Programming (OOP) that is essential for designing systems. It involves representing essential features of real-world entities while hiding unnecessary details. Here’s how abstraction is used in designing systems:

• Abstraction involves focusing on the essential characteristics of an object or system while ignoring irrelevant details. It allows developers to create models that capture the essence of real-world entities and concepts in a simplified and manageable way.
• In OOP, abstraction is often achieved through class abstraction. Classes encapsulate data (attributes) and behavior (methods) related to a specific entity or concept, abstracting away the implementation details. Users of the class interact with it through its public interface, without needing to know how its functionality is implemented internally.

Encapsulation is a fundamental concept in Object-Oriented Programming (OOP) that plays a crucial role in designing systems. It involves bundling data (attributes) and methods (functions) that operate on the data into a single unit, called a class. Here’s how encapsulation is used in designing systems:

• Encapsulation refers to the bundling of data and methods that manipulate the data into a single unit, known as a class. It allows for the implementation details of a class to be hidden from external users, who interact with the class through its public interface.
• In OOP, classes serve as units of encapsulation. They encapsulate data (attributes) and behavior (methods) related to a specific entity or concept. Users of the class interact with it through its public methods, without needing to know the internal details of how those methods are implemented.
• Encapsulation is enforced through access modifiers, such as public, private, and protected, which control the visibility of class members (attributes and methods). Public members are accessible from outside the class, while private members are only accessible from within the class itself. This allows for better control over the access to and manipulation of the class’s internal state.

In Object-Oriented Programming (OOP), relationships between classes are fundamental for designing systems that accurately model real-world scenarios. These relationships help define how classes interact with each other and how they collaborate to achieve the system’s functionality. Here are some common relationships between classes in OOP system design:

• Association represents a relationship between two classes where one class uses another class as part of its functionality. It can be either a one-to-one, one-to-many, or many-to-many relationship. For example, in a library management system, a “Library” class might be associated with multiple “Book” classes.
• Aggregation is a type of association where one class represents a “whole” and contains instances of another class, which represents its “parts.” The parts can exist independently of the whole. For example, a “Car” class may contain instances of a “Wheel” class.
• Composition is a stronger form of aggregation where the parts are essential to the existence of the whole. If the whole is destroyed, the parts are also destroyed. For example, a “House” class may be composed of “Room” objects.
• Inheritance represents an “is-a” relationship between classes, where one class (subclass or derived class) inherits properties and behaviors from another class (superclass or base class). It allows for code reuse and promotes a hierarchical organization of classes.
• Dependency represents a weaker relationship between classes where one class relies on another class, typically through method parameters or return types. Changes in the dependent class may affect the dependent class, but not vice versa.
• Association represents a relationship between classes based on their usage, while inheritance represents a relationship based on specialization and generalization. Association is typically used when one class needs to use the functionality of another class, while inheritance is used when one class is a more specific type of another class.

The SOLID principles are a set of design principles in Object-Oriented Programming (OOP) that aim to create more maintainable, flexible, and scalable software systems. Each principle focuses on a specific aspect of OOP design and encourages practices that lead to better code organization, reusability, and extensibility. Here’s an overview of the SOLID principles and how they apply to designing systems:

• SRP states that a class should have only one reason to change, meaning it should have only one responsibility or job. This principle promotes high cohesion and low coupling by ensuring that classes are focused and do not become overly complex.
• OCP states that classes should be open for extension but closed for modification. This means that the behavior of a class should be easily extendable without modifying its source code. This principle promotes code reuse and minimizes the risk of introducing bugs when making changes.
• LSP states that objects of a superclass should be replaceable with objects of its subclasses without affecting the correctness of the program. In other words, subclasses should be able to substitute their superclass without changing the behavior expected by clients.
• ISP states that clients should not be forced to depend on interfaces they don’t use. It encourages the creation of smaller, more focused interfaces tailored to specific client requirements, rather than large, monolithic interfaces that cater to multiple clients.
• DIP states that high-level modules should not depend on low-level modules; both should depend on abstractions. It promotes loose coupling by allowing modules to depend on abstractions rather than concrete implementations. This facilitates easier modification and testing of code.

By adhering to the SOLID principles, developers can create software systems that are easier to understand, maintain, and extend over time. These principles provide guidelines for designing classes and relationships between them that promote modularity, flexibility, and scalability, leading to more robust and maintainable codebases.

here are some real-world examples of Object-Oriented Programming (OOP) used in system design:

• In a banking system, OOP principles are applied to model entities such as accounts, customers, transactions, and branches.
• Each of these entities can be represented as objects with attributes and methods. For example, a “BankAccount” class might have attributes like account number, balance, and owner, with methods for depositing, withdrawing, and transferring funds.
• In an e-commerce platform, OOP is used to model entities such as products, customers, orders, and payments.
• Each product can be represented as an object with attributes like name, price, and quantity in stock.
• Customers can be represented as objects with attributes like name, address, and payment information. Orders can be represented as objects containing information about the products purchased and the associated customer.
• In a hospital management system, OOP principles are used to model entities such as patients, doctors, appointments, and medical records.
• Each patient can be represented as an object with attributes like name, age, and medical history. Doctors can be represented as objects with attributes like name, specialization, and availability.
• Appointments can be represented as objects containing information about the patient, doctor, and scheduled time.

These examples illustrate how Object-Oriented Programming is applied in various domains to model real-world entities and their interactions. OOP principles such as encapsulation, inheritance, polymorphism, and abstraction help in designing modular, scalable, and maintainable systems that accurately represent complex real-world scenarios.

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