to problem solving and programming

Great article regarding problem solving skill, informative and motivating both.

Codility Tests

Outstanding post, I believe people should larn a lot from this website, its really user pleasant.

Technical Screening Tools

I was very happy to discover this great site. I need to thank you for your time just for this fantastic read!

Sharifa Ismail Yusuf

I learnt from this article that one of the key skills a developer need to have is the \"problem solving skills\". Developers also need dedication, time, create time to practice so they can improve their problem solving skills constantly. I do ask for help from others and learn from past solutions and apply them to new problems. From what I have learnt so far, I will try my best to start focusing on building and practicing Flexibility, critical thinking, communication and team work. Solve a lot of problems on a lot of different platforms. Solve problems on context other than work. To start carring out the above, I will schedule time in a week or everyday to conciously practice problem solving skills and other related problem solving skills.Thanks alot for this wonderful article!

dewayne sewell

Ive learnt the skill of problem solving is like a muscle, where it is important to keep exercising it to stay strong. It is important to be aware of the soft skills necessary for effective problem solving also, such as communication, critical thinking, team working that can leverage your technical hard skills to find a solution faster/more effective. Two things I will aim to do is; 1. To solve problems on different platforms so I don’t get too comfortable on only one and stagnate. This not only challenges the brain to see things from a new perspective, but to start the habit of continuous learning and skill building. 2. Reach out to others for help / discuss problems and my solutions for feedback and advice and sharing ideas.

Pakize Bozkurt

Problem solving skills is a crucial thing to make easier or better your life. In fact as a human being we do it in every day life. I mean, we have to do it for living. There are many ways to how to do it. The best way is we should ask right questions. First of all, we should ask some questions, such as; \' Are we aware of the problem?, Are we clarify the problem? Do we go into problem rational? Do we have reasons? or Do we have evidences? Do we do check them out? etc. I am from Philosophy teacher background. I like solving problem whatever in my work or daily life. Secondly, we should have more perspectives . Although our brain is lazy, it is always in a starvation for knowledge.For this there are many enjoyable things to do it. I highly recommend to read book every day which kind of you like it and playing game or solving puzzle. I love solving Sudoku, puzzle and reading book or article. Finally, solving problem is our invatiable needed. Having flexibility, critical thinking, communication and teamwork are easy way to improve us to how we can do our work better and good life. Massive thank for this amazing article!

I read this amazing article. Normally, everyone knows that but we dont use most of time this informations. Which one is the best way to use? Really it does not matter, every one is like a gold opinion. We can use this ideas for the daily life. I have already used that learn from past solution and ask to someone who knows very well. This is so helpful for me. Sometimes google is the best option for ask to someone. Google can be the best teacher for us as well. Soft skills like a team work or solving problem and critical thinking can be important than typing code. We can learn typing code but we can not learn critical thinking and solving problems from google very well. Thank you for this article.

Ipsa iure sed rerum

Excepturi quo volupt

Thanks for this !

Fahil kiima

Thanks a lot for the ideas on problem solving,I really had a problem with that and now going to use what you\'ve informed us about to better my problem solving skills. Thanks.

Alan Codinho

Nice overview

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A Guide to Problem-Solving for Software Developers with Examples

If I ask you, out of the blue, what’s the role of a developer, what would you answer? Coding all day? Drinking coffee? Complaining about the management?

To me, a developer is first and foremost a problem solver, simply because solving problem is the most important (and the most difficult) part of our job. After all, even if our code is perfect, clear, performing great, a masterpiece of form and meaning, it’s useless if it doesn’t solve the problem it was meant to solve.

So, let’s dive into problem-solving today. More specifically, we’ll see in this article:

• How to define a problem, and the difference sometimes made between problem-solving and decision-making.
• Why some problems should not be solved.
• The two wide categories of problems you can encounter.
• Why it’s important to correctly define the problem, and how to do so.
• How to explore the solution space.
• Why deferring a problem might be the best decision to make in specific situations.
• Why reflecting on the whole process afterward can help you in the future.

This article is mostly based on my own experience, even if I apply here some ideas I found in books and papers.

We have our plan. Now, it’s time to dive deep into the difficult, but rewarding, process of problem-solving.

Problem-Solving and Decision-Making

“When I use a word,” Humpty Dumpty said in rather a scornful tone, “it means just what I choose it to mean — neither more nor less.” “The question is,” said Alice, “whether you can make words mean so many different things.” “The question is,” said Humpty Dumpty, “which is to be master — that’s all.” Lewis Caroll Source

Words are ambiguous; they can mean different things for each of us. So let’s first begin to agree on the definition of “problem-solving” here, to be sure we’re on the same page.

Let’s first look at the definition of the word “problem” in a dictionary:

• According to the American Heritage Dictionary , a problem is “a question to be considered, solved, or answered”.
• According to the Oxford Learner’s dictionary , a problem is “a thing that is difficult to deal with or to understand”.

In short, in any problem, there is some degree of uncertainty. If you’re certain of the solution, the problem is already solved. Nothing would need to be “considered, solved, or answered”.

Information is useful to reduce this uncertainty. The quantity is often not the most important, but the quality will be decisive. If I tell you that 90% of my readers are extremely intelligent, would it help you to solve a problem in your daily job? I bet it wouldn’t. It’s information nonetheless, but its usefulness for you is close to zero.

This is an extreme example, but it highlights an important point: before collecting any data, define your problem clearly; then, according to the problem, decide what data you need. Yet, many companies out there begin to collect the data and then decide what problem to solve. We’ll come back to that soon in this article.

So, to summarize, a problem is a situation with some degree of uncertainty. Sometimes, this uncertainty needs to be reduced to come up with an appropriate solution, or, at least, a decision to move forward to your specific goal.

Is there a Problem to Solve?

Whenever you (or somebody else) see a problem, you should always ask yourself this simple question first: is it really a problem, and should we solve it now ?

In other words, ask yourself the following questions:

• Why is this problem important to solve?
• Would be solving the problem creates some value? What value?
• What would happen if the problem was not solved?
• What desired outcome do we expect by solving the problem?

If the problem doesn’t bother anybody and solving it doesn’t create any value, why allocating effort and time to solve it?

It sounds obvious, but it’s an important point nonetheless. More often than not, I see developers heading first in solving problems without asking themselves if they should solve them at the first place.

The most common examples I can think of are useless refactoring. I saw developers refactoring parts of codebases which never change, or is rarely executed at runtime. In the mind of the developer, the code itself is the problem: refactoring is the solution.

I remember a similar case: a developer refactored part of the codebase which was basically never used. We discovered, months later, when we had more and more users using this specific part of the codebase, that the refactoring didn’t really simplify anything. To the contrary; we had to refactor the code again. The first refactoring tried to solve a problem which didn’t exists.

Of course, the developer could argue that the value created is a “cleaner” codebase, but it’s arguable, especially when the code is neither often modified nor used. The value created here is not clear, and it would have been easier if the first refactoring never happened. In this specific situation, I recommend refactoring when you actively change part of the codebase for another reason (implementing a new feature for example).

Whether a problem is worthy to be solved is subjective. It also depends on the problem: if the solution is clear and straightforward, it might be useful to solve it, if the consequences of the solution are also clearly known and the risks are low. Unfortunately, these kinds of problems, in practice, are quite rare.

Types of Problems

I would define here two wide categories of problems: the problems with a (or multiple) clear solution (what the literature call “problem-solving”), and the problems without clear solution (it’s sometimes called “decision-making” instead of “problem-solving”).

In fact, if the problem you’re trying to solve has a clear, accepted answer, it’s very likely it has been solved already. It’s often the case for mechanical, technical problems. For example, let’s say that you need to order a list; you just have to search on the wild Internet how to do so in your programming language of choice, and you’re done! You can ask an “AI” too, or stack overflow, or whatever.

In my experience, most technical problems have one (or multiple) accepted solution. I won’t speak about these kinds of problems at length in this article, since they’re the easiest to solve.

When you’re in front of a problem which has no clear solution (even after doing some research), it’s where things get more complicated. I’d argue that most problems you’ll face, as a software developer, are of this category. Problems which are directly linked to the domain of the company you work with are often specific (because they depend on the domain), and complex.

For example, I’m working for a company providing a learning platform for medical students who want to become doctors, among other services. This context is changing because the real world is changing; medicine is no exception.

Recently, we had to create new data structures for the knowledge we provide; these data structures are directly linked to the domain (medicine) here. But what data structures to create? How can they adapt to the ever-changing environment? How to capture the data in the most meaningful way, with understandable naming for other developers?

Decisions had to be made, and when there are no clear solutions, you need to come up with a couple of hypothesizes. They won’t feel necessary like solutions , but rather decisions to take to move forward toward the desired outcome. It often ends up in compromises, especially if you’re working in a team where the members have different opinions .

Also, architectural decisions have often no clear solutions because they depend, again, on the changing context. How to be sure that an architectural decision is good today and in three months? How can we make the architecture flexible enough to adapt to the blurry future?

As developers, we deal with complex codebases, which are somewhat linked to the even more complex real world. It’s difficult to know beforehand the consequences of our decisions, as well as the benefits, the drawback, and the potential bugs we introduce.

Before jumping into the solution space however, we first need a good detour in the problem space.

Defining the Problem

Correctly stating the problem.

After determining that we indeed have some kind of problem, it’s tempting to try to find a solution directly. Be patient: it’s better to look at the problem more closely first.

If you don’t specify well the problem, you might not solve it entirely. It’s also possible that you end up solving the wrong problem, or the symptoms of a problem, that is, other minor problems created by a root problem. Often, the ideal scenario is to find the root problem, even if you don’t want to tackle it first. In any case, it’s always useful information.

For example, not long ago, our users didn’t find the content they were searching for, using our search functionality on our learning platform.

We could have directly solved the problem by asking the search team to adjust that for us, but this problem was only a symptom. It wasn’t the first time that we had to spend time and energy trying to communicate to the search team what we wanted to fix; the real root problem here was that we didn’t have any ownership of our search results.

The solution: we created a better API communicating with the search team, to be able to adjust ourselves the search results in a more flexible manner.

When looking at a problem, a good first step is to write it down. Don’t do it once; try to find different formulations for the same problem.

Writing is nice (I love it!), but other ways to represent ideas can be really useful too. You can try to draw what you understand from the problem: a drawing, a diagram, or even a picture can help you understand the problem.

From there, you can ask yourself: do you have enough information to take a decision? The answer will be mostly based on the experience of the problem solver, there is no magical formula to be sure that you can and will solve the problem.

You should also try to look at the problem from different angles, to really frame it correctly. The best way to do so is to solve problems as a team.

Solving Problems in a Team

Trying to describe and think about a problem is a great beginning, but it’s even better if you do it as a team. You can exchange experience, opinions, and it’s easier to look at a problem from multiple angles when multiple developers are involved.

First, make sure that everybody in the team is aware of the problem. Defining it altogether is the best. If you have a doubt that somebody is not on the same page, you can re-explain it using different words. It might bring more insights and ideas to the discussion.

Don’t assume that everybody understands the problem equally. Words are powerful, but they are also ambiguous; never hesitate to ask questions (even if they seem stupid at first), and encourage the team to do the same. If your colleagues see that you’re not afraid to ask, it will give them confidence to do the same.

The ambiguity can also build overtime, after the problem was discussed. That’s why it’s really important to document the whole process, for anybody to be able to look at it again and fix the possible creeping misconceptions. Don’t try to describe everything, but try to be specific enough. It’s a delicate balance, and you’ll get better at it with experience.

If you don’t like writing, I’d recommend you to try anyway: this is a powerful skill which will be useful in many areas of your life.

Regarding the team of problem solvers, diversity is important. Diversity of opinion, experience, background, you name it. The more diverse the opinions and ideas are, the more chances you’ll have to solve the problem satisfyingly (more on that later). If the members of the team have enough respect, humility, and know how to listen to their colleagues , you’re in the perfect environment to solve problems.

As developers, we’re dealing with moving systems, because they need to reflect the ever-changing business domain of the company you’re working with. These problems are unique, and even if similar problems might have been solved in the past, they’re never the exactly same. The differences can have an impact on the solution, sometimes insignificant (allowing you to re-apply the solution found previously), sometimes important enough to change the solution entirely.

Exploring the Solution Space

Now that we’ve defined the problem, thought about it with our team, tried to look at it from different angles, it’s time to try to find solutions, or at least to make a decision.

What is a good decision? The one which will bring you closer to your desired outcome. It sounds obvious, but there can be some ego involved in discussions, which will push us to try to be right even if it’s not the best solution in the current context. Our personal incentives can conflict with the company’s best interest; it’s always good to try to stay aware of that.

The solution should also be the simplest possible, while still moving forward to the desired outcome. It should also have an acceptable level of risk when we decide to apply the solution. In my experience, complicated solutions are the ones which come up first: don’t stop there. Take some time trying to find the best solution with your team.

For example, here’s what we do with my actual team:

• We define the problem altogether.
• We try to think about different hypothesizes. Not only one, but a couple of them.
• We write the benefits and drawbacks of each hypothesis (which can lead to more ideas, and possibly more hypothesizes).
• We commit to a hypothesis, which then needs to be implemented.

What I meant by “hypothesis” here is a solution which might work; but only the implementation of the hypothesis can be considered as a solution. Before the implementation, it’s just an informed guess. Many things can go wrong during an implementation.

This process looks simple, but when you have multiple developers involved, it’s not. Again, if each member of the team have good soft skills and some experience, it can be an enjoyable and rewarding process. But you need a good team for it to work efficiently (that’s why it’s so important to ask the good questions when joining a company). It’s even better if the members of the team are used to swim in uncertainty, and take it as a challenge more than a chore.

The process described above is just an example; in practice it’s often more chaotic. For example, even when a decision is made, your brain might still continue to process the problem passively. If you find some flaws in the hypothesis you’ve committed to, congratulations! You have now a brand-new problem.

I can’t emphasize it enough: try to be as detached as possible from your ideas, opinions, and preferred hypothesizes. The goal is not for you to be right and feel good, but for your company to move in the good direction. It’s hard, but with practice it gets easier.

I also want to underline the importance of finding both benefits and drawbacks for the different hypothesizes you (and your team) came up with.

To find good solutions, we might also need to reduce the uncertainty around their possible consequences. Doing some external research can help, like gathering data around the problem and the possible hypothesizes. In the best case scenario, if you can find enough data, and if you feel confident that you can move forward with a hypothesis, that’s already a great victory.

If you don’t have enough external information to reduce the uncertainty to a level you feel comfortable with, look at your past experience. Try to find problems similar to the one your deal with in the present, and try to think about the solutions applied at the time, to see if they could also be applied in your current case. But be careful with this approach: complex problems are context-sensitive, and the context you were in the past will never be exactly the same as the present and future contexts.

For example, I recently changed the way we display search results in our system, because we had some data indicating that some users had difficulties to find what they really wanted to find. The problem: users have difficulties to find the good information; it’s a recurrent problem which might never be 100% solved. That said, thanks to the data gathered, we found an easy way to improve the situation.

The data was very clear and specific, but it’s not always the case. More often than not, your data won’t really prove anything. It might only show correlations without clear causality. It will be even more true if you begin by gathering data without defining first the problem you try to solve. You can find problems looking at some data, that’s true, but it needs care and deep understanding of what you’re doing; looking at data when you know exactly what you want to solve works better.

Using this kind of process, the hypothesis is often some sort of compromise. That’s fine; committing to a hypothesis is not the end of the process, and there will be other occasions to revisit and refine the solution.

If you don’t feel comfortable with the level of uncertainty of the problem (or the risk involved by applying your hypothesis), you need to dig more. Writing a prototype can be useful for example, if you hesitate between two or more approaches. If your prototype is convincing enough, it can also be useful to gather feedback from your users, even if the ones testing your hypothesis will always be more invested if they test a real-life functionality, instead of a prototype which might use dummy data, or be in a context which is too remote from the “real” context.

In my opinion, prototypes are not always useful for complex problems, because a prototype only test a new feature at time T, but doesn’t allow you to see if the solution stay flexible enough overtime. That’s often a big concern: how will the solution evolve?

But prototyping can still help gather information and reduce the uncertainty of the problem, even if the prototype doesn’t really give you the solution on a silver platter. It’s also great for A/B testing, when you’re in the (likely) case when you have not much information about the real needs of your users. You could ask them of course, but nothing guarantee that they know themselves what these needs are.

If you don’t find any satisfying hypothesis to your problem, you might also challenge the desired outcome. Maybe a similar, simplest hypothesis, with slightly different outcomes, could work better? If it makes things easier, faster, and less complex, it could be the best solution. Don’t hesitate to challenge your stakeholders directly on the desired outcomes.

Deferring the Problem

In some cases, you might be hesitant to try to solve a problem if there is still too much uncertainty around it. In that case, it might be best to defer solving the problem altogether.

Deferring the problem means that you don’t solve it now ; you keep things as they are, until you get more information to reduce the uncertainty enough.

We had a problem in the company I worked with some time ago: we have dosages which can be discovered in articles, but users didn’t really find them, and nobody really knew why. Because of this lack of information, the problem was not tackled right away, but differed. From there, data have been collected overtime, allowing us to understand the scope of the problem better.

Don’t forget that deferring a problem is already taking a decision. It might be the less disruptive decision for the application and its codebase, but it’s s decision nonetheless, and it can have consequences. Seeing a differed problem as a decision will push you to think about the possible consequences of your inaction, and you’ll look at it as a partial “solution”, with some uncertainty and risk associated to it.

In my experience, deferring the problem works well only when you try to actively seek more data to solve it later. It can be some monitoring to see how the problem evolves, or some data taken from users’ actions. Sometimes, simply waiting can also give you important information about the nature of the problem.

What you shouldn’t do is try to forget the problem. It might come back in force to haunt your sleepless nightmares later. Avoiding a problem is not deferring it.

Here’s another example: we began recently to build some CMS tooling for medical editors, for them to write and edit content on our learning platform. We had one GraphQL API endpoint at the beginning, providing data to two different part of the application:

• Our CMS for medical editors.
• Our learning platform for medical students.

We knew that using one single GraphQL endpoint for these two types of users could cause some problems.

But we didn’t do anything about it, mostly because we didn’t see any real, concrete problem, at least at first. When a minor symptom, related to this unique endpoint, popped up, we spoke about it, and we still chose not to do anything. We preferred deferring the problem once more, to try to solve the real problem (one API for two different kinds of applications) later.

Finally, when we had enough symptoms and some frustration, we decided to split our graphQL API in two different endpoints. It was the best moment to do so: we had enough information to come up with a good decision, we applied it, and we stayed vigilant, to see how our applied hypothesis would evolve.

Moving fast and breaking things is not always the best solution. In some situations, waiting a bit and see how things evolve can allow you to solve your problems in a more effective way. But, as always, it depends on the problem, its context, and so on.

Reading this article, you might have wondered: how much information is enough to be comfortable enough to apply a solution? Well, again, your experience will be the best judge here. You’ll also need to consider carefully risks, benefits, and drawbacks. It doesn’t mean that you need to chicken out if you don’t have 100% certainty about a problem and some hypothesizes; being a software developer implies to have some courage and accept that mistakes will be made. It’s not an easy task, and there is no general process to follow in any possible case.

In short: use your brain. Even if you’re totally wrong, you’ll have the opportunity to fix the bad decisions you’ve made before the implementation, during the implementation, and even after it. We don’t code in stone.

The Implementation: The Value of Iteration

You’ve gathered with your team, tried to define the problem, found multiple hypothesizes, and agreed to try one of them. Great! Problem solved.

Not so fast! We still need to apply the hypothesis, and hope that it will become a good solution to the problem. Doing so, you’ll gather more information along the way, which might change your perspective on the problem, on your hypothesizes, and can even create some baby problems on its own.

It’s where the agile methodology is useful: since we’ll never have 100% certainty regarding a problem and its possible solution, we’ll learn more about both while implementing the hypothesis. That’s why it’s so valuable to iterate on the implementation: it gives you more information to possibly adjust your code, or even the problem, or even switching hypothesizes altogether. Who knows? A solution which is not implemented is just a guess.

If the hypothesis applied is not the ones you would have personally preferred (compromising, or even giving up on your preferred solution is common in a team), only applying it will tell you if you’re right or wrong; that is, if the hypothesis can become a solution solving the problem, at least in the present context.

If you’re worried about how a specific solution will evolve overtime, it’s more complicated, because an implementation won’t give you the information you seek. Still, implementing a hypothesis can be a great source of learning (the most valuable to me is when I’m wrong, because I learn even more). If you think that your hypothesis can have better outcome at time T, you might also try to implement it and compare it. Again, it’s where prototyping is useful.

When applying the solution, you need to look at the details of the implementation, as well as the big picture, to judge if the solution you’re creating is appropriate (leading to the desired outcome). This is a difficult exercise. In general, a developer should be able to reason on different levels of abstraction, more or less at the same time. Again, if you’re aware of it, your experience will help you here, and you can also push yourself to think of all the possible risks and consequences at different levels.

If you work in a team, try to participate (at least a bit) into the implementation of the solution. It’s not good to create silos in teams (that is, only a couple of members have some information others don’t have).

You can go as far as looking at other projects, and ask yourselves these questions:

• Did we had similar problems on these other projects? How did we solve them?
• What was the context of these projects? Is it similar to our current context?
• What did we learn from these other problems, and their implementation? Is the implementation similar to what we’re doing now?

In any case, I would definitely recommend you to write a development journal. I write mine for years, and it has been valuable in many cases. I basically write in there:

• The interesting problems I had.
• The decisions made.
• How the implementation of the solution evolved overtime.
• The possible mistakes we made along the way.

It’s a great resource when you have a problem and you want to look at your past experience.

To evaluate your decisions overtime, nothing will beat a good monitoring process: logs, tests, and so on. It’s what the book Building Evolutionary Architecture call “fitness functions” for example, some monitoring allowing you to measure how healthy your architecture stays overtime. It doesn’t have to stop to the architecture; you can think about different monitoring system to see how something evolve, especially if the solution has still a lot of uncertainty regarding its benefits, drawbacks, and risks.

You can also do that retrospectively: looking at how the code complexity evolve overtime using Git for example.

Retrospective on the Process

We defined the problem, implemented a solution iteratively, and now the problem is gone. That’s it! We made it! Are we done now?

Decisions are sometimes not optimal, and implementing a solution successfully doesn’t mean that there wasn’t a better (simpler) one to begin with. That’s why it can be beneficial to look back and understand what went right, and what went wrong. For example, we can ask ourselves these questions:

• Looking at what we learned during the whole process, is there a potentially better hypothesis to solve the problem in a simpler, more robust way?
• What are the benefits and drawbacks we missed when speaking about the different hypothesizes, but we discovered during the implementation? Why we didn’t think about them beforehand?
• What other problems did we encounter during the implementation? Did we solve them? Did we differ some? What should be the next steps regarding these new problems?
• What kind of monitoring did we put in place to make sure that the solution won’t have undesired outcomes overtime? Can we learn something with this data?

Reflecting on past solutions is a difficult thing to do. There is no way to logically assess that the decision taken was better than others, since we didn’t implement the other hypothesizes, and we didn’t look at them overtime to appreciate their consequences. But you can still look at the implementation of the solution overtime, and write in your developer journal each time there is a bug which seems directly related to the solution. Would the bugs be the same if another solution would had been applied?

Bugs are often not an option; they will pop up, eventually. Nonetheless, it’s important to make sure that you can fix them in a reasonable amount of time, and that you don’t see them creeping back in the codebase after being solved. Some metrics, from the DevOps movement (like MTTR for example) can help here. Sometimes, bugs will show you a better, more refined solution to the original problem; after all, bugs can also give you some useful information. They are also the most direct result of the implementation of your solution.

If you want to know more about measuring complexity (which can be also used to measure complexity overtime after applying a solution), I wrote a couple of articles on the subject .

Humility in Problem-Solving

It’s time to do a little summary. What did we see in this article?

• We need to ensure that the problem we found is really a problem we need to solve. Is there any value to solve the problem? Is it even a problem?
• Try to determine what kind of problem you have: a problem which can have multiple, specific, known answers (like a technical problem), or a problem which depends on the real-life context, without known solutions?
• Defining the problem is important. Try to define it using different words. Write these definitions down. Does everybody in your team understand the problem equally?
• It’s time to explore the solution space. Draft a couple of hypothesizes, their benefits, drawbacks, and risks. You can also do some prototyping if you think it would give you more information to take the best decision.
• Do you have enough information to implement a hypothesis, becoming effectively a solution? If it’s not the case, it might be better to keep the status quo and try to solve the problem later, when you’ll have more information. But don’t forget the problem!
• If you decide to implement a solution, do it step by step, especially if you’re unsure about the consequences of your decisions. Implement an independent part of the hypothesis, look at the consequences, adjust if necessary, and re-iterate.
• When the solution is implemented, it’s time to reflect on the whole process: did we solve the problem? What other problems did we encounter? Maybe another solution would have been better? Why?

As I was writing above, most problems you’ll encounter will be complex ones, embedded into a changing environment with different moving parts. As a result, it’s difficult to train to solve problems in a vacuum; the only good training I know is solving real life problems. That’s why your experience is so important.

Experience build your intuition, which in turn increase your expertise.

You’ll never have 100% certainty that a solution will bring you the desired outcome, especially if you are in front of a complex problem with a blurry context. If you are absolutely convinced that you have the good solution without even beginning to implement it, I’d advise you to stay humber in front of the Gods of Complexity, or they will show you how little you know.

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This comprehensive guide of Programming Tutorial or Coding Tutorial provides an introduction to programming, covering basic concepts, setting up your development environment, and common beginner problems. Learn about variables, data types, control flow statements, functions, and how to write your first code in various languages. Explore resources and tips to help you to begin your programming journey. We designed this Programming Tutorial or Coding Tutorial to empower beginners and equip them with the knowledge and resources they will need to get started with programming.

Programming Tutorial

1. What is Programming?

Programming , also known as coding , is the process of creating a set of instructions that tell a computer how to perform a specific task. These instructions, called programs, are written in a language that the computer can understand and execute.

Table of Content

• What is Programming?
• Getting Started with Programming
• Common Programming Mistakes and How to Avoid Them
• Basic Programming EssentialsA Beginner’s Guide to Programming Fundamentals
• Advanced Programming Concepts
• Writing Your First Code
• Top 20 Programs to get started with Coding/Programming
• Next Steps after learning basic Coding/Programming
• Resources and Further Learning
• Frequently Asked Questions (FAQs) on Programming Tutorial

Think of programming as giving commands to a robot. You tell the robot what to do, step-by-step, and it follows your instructions precisely. Similarly, you tell the computer what to do through code, and it performs those tasks as instructed.

The purpose of programming is to solve problems and automate tasks. By creating programs, we can instruct computers to perform a wide range of activities, from simple calculations to complex tasks like managing databases and designing video games.

A. How Programming Works:

Programming involves several key steps:

• Problem definition: Clearly define the problem you want to solve and what you want the program to achieve.
• Algorithm design: Develop a step-by-step procedure for solving the problem.
• Coding: Translate the algorithm into a programming language using a text editor or integrated development environment (IDE).
• Testing and debugging: Run the program and identify and fix any errors.
• Deployment: Share the program with others or use it for your own purposes.

B. Benefits of Learning to Code:

Learning to code offers numerous benefits, both personal and professional:

• Develop critical thinking and problem-solving skills: Programming encourages logical thinking, problem decomposition, and finding creative solutions.
• Boost your creativity and innovation: Coding empowers you to build your own tools and applications, turning ideas into reality.
• Increase your employability: The demand for skilled programmers is high and growing across various industries.
• Improve your communication and collaboration skills: Working with code often requires collaboration and clear communication.
• Gain a deeper understanding of technology: Learning to code gives you a better understanding of how computers work and how they are used in the world around you.
• Build self-confidence and motivation: Successfully completing programming projects can boost your confidence and motivate you to learn new things.

Whether you’re interested in pursuing a career in technology or simply want to expand your knowledge and skills, learning to code is a valuable investment in your future.

2. Getting Started with Programming Tutorial

A. choosing your first language.

Assess Resource Availability:

• Free Online Resources: Platforms like Geeksforgeeks, Coursera, edX, and Udemy offer structured learning paths for various languages.
• Paid Online Courses: Platforms like Geeksforgeeks, Coursera, edX, and Udemy offer structured learning paths for various languages.
• Books and eBooks: Numerous beginner-friendly books and ebooks are available for most popular languages.
• Community Support: Look for active online forums, communities, and Stack Overflow for troubleshooting and questions.

B. Which Programming Language should you choose as your First Language?

Here’s a breakdown of popular beginner-friendly languages with their Strengths and Weaknesses:

C. Setting Up Your Development Environment

Choose a Text Editor or IDE :

• Text Editors: Sublime Text, Atom, Notepad++ (lightweight, good for beginners)
• Offline IDEs: Visual Studio Code, PyCharm, IntelliJ IDEA (feature-rich, recommended for larger projects)
• Online IDEs: GeeksforGeeks IDE

Install a Compiler or Interpreter:

• Compilers: Convert code to machine language (C++, Java)
• I nterpreters: Execute code line by line (Python, JavaScript)

Download Additional Software (if needed):

• Web browsers (Chromium, Firefox) for web development
• Android Studio or Xcode for mobile app development
• Game engines (Unity, Unreal Engine) for game development

Test Your Environment:

• Write a simple program (e.g., print “Hello, world!”)
• Run the program and verify the output
• Ensure everything is set up correctly
• Start with a simple editor like Sublime Text for code basics.
• Use an IDE like Visual Studio Code for larger projects with advanced features.
• Join online communities or forums for help with setup issues.

3. Common Programming Mistakes and How to Avoid Them

• Syntax errors: Typographical errors or incorrect grammar in your code.
• Use syntax highlighting in your editor or IDE.
• Logical errors: Errors in the logic of your program, causing it to produce the wrong results.
• Carefully review your code and logic.
• Test your program thoroughly with different inputs.
• Use debugging tools to identify and fix issues.
• Runtime errors: Errors that occur during program execution due to unforeseen circumstances.
• Seek help from online communities or forums for specific errors.
• Start with simple programs and gradually increase complexity.
• Write clean and well-formatted code for better readability.
• Use comments to explain your code and logic.
• Practice regularly and don’t be afraid to experiment.
• Seek help from online communities or mentors when stuck.

4. Basic Programming Essentials – A Beginner’s Guide to Programming Fundamentals:

This section delves deeper into fundamental programming concepts that form the building blocks of any program.

A. Variables and Data Types:

Understanding Variable Declaration and Usage:

• Variables are containers that hold data and can be assigned different values during program execution.
• To declare a variable, you specify its name and data type, followed by an optional assignment statement.
• Example: age = 25 (declares a variable named age of type integer and assigns it the value 25).
• Variables can be reassigned new values throughout the program.

Exploring Different Data Types:

• Integers: Whole numbers without decimal points (e.g., 1, 2, -3).
• Floats: Decimal numbers with a fractional part (e.g., 3.14, 10.5).
• Booleans: True or False values used for conditions.
• Characters: Single letters or symbols (‘a’, ‘$’, ‘#’).
• Strings: Sequences of characters (“Hello, world!”).
• Other data types: Arrays, lists, dictionaries, etc. (depending on the language).

Operations with Different Data Types:

Each data type has supported operations.

• Arithmetic operators (+, -, *, /) work with integers and floats.
• Comparison operators (==, !=, >, <, >=, <=) compare values.
• Logical operators (&&, ||, !) combine conditions.
• Concatenation (+) joins strings.
• Operations with incompatible data types may lead to errors.

B. Operators and Expressions:

Arithmetic Operators:

• Perform basic mathematical calculations (+, -, *, /, %, **, //).
• % (modulo) returns the remainder after division.
• ** (power) raises a number to a certain power.
• // (floor division) discards the fractional part of the result.

Comparison Operators:

• Evaluate conditions and return True or False.
• == (equal), != (not equal), > (greater than), < (less than), >= (greater than or equal), <= (less than or equal).

Logical Operators: Combine conditions and produce True or False.

• && (and): both conditions must be True.
• || (or): at least one condition must be True.
• ! (not): reverses the truth value of a condition.

Building Expressions:

• Combine variables, operators, and constants to form expressions.
• Expressions evaluate to a single value. Example: result = age + 10 * 2 (calculates the sum of age and 20).

C. Control Flow Statements:

Conditional Statements: Control the flow of execution based on conditions.

• if-else: Executes one block of code if the condition is True and another if it’s False.
• switch-case: Executes different code blocks depending on the value of a variable.

Looping Statements: Repeat a block of code multiple times.

• for: Executes a block a specific number of times.
• while: Executes a block while a condition is True.
• do-while: Executes a block at least once and then repeats while a condition is True.

Nested Loops and Conditional Statements:

• Can be combined to create complex control flow structures.
• Inner loops run inside outer loops, allowing for nested logic.

D. Functions:

Defining and Calling Functions:

• Blocks of code that perform a specific task.
• Defined with a function name, parameters (optional), and a code block.
• Called throughout the program to execute the defined functionality.

Passing Arguments to Functions:

• Values passed to functions for processing.

Returning Values from Functions:

• Functions can return a value after execution.
• Useful for collecting results.
• A function calling itself with a modified input.
• Useful for solving problems that involve repetitive tasks with smaller inputs.

These topics provide a solid foundation for understanding programming fundamentals. Remember to practice writing code and experiment with different concepts to solidify your learning.

5. Advanced Programming Concepts

This section explores more advanced programming concepts that build upon the foundational knowledge covered earlier.

A. Object-Oriented Programming (OOP)

OOP is a programming paradigm that emphasizes the use of objects to represent real-world entities and their relationships.

1. Classes and Objects:

• Classes: Define the blueprint for objects, specifying their properties (attributes) and behaviors (methods).
• Objects: Instances of a class, with their own set of properties and methods.

2. Inheritance and Polymorphism:

• Inheritance: Allows creating new classes that inherit properties and methods from existing classes (superclasses).
• Polymorphism: Enables objects to respond differently to the same message depending on their type.

3. Encapsulation and Abstraction:

• Encapsulation: Encloses an object’s internal state and methods, hiding implementation details and exposing only a public interface.
• Abstraction: Focuses on the essential features and functionalities of an object, ignoring unnecessary details.

B. Concurrency and Parallelism

Concurrency and parallelism are crucial for improving program efficiency and responsiveness.

1. Multithreading and Multiprocessing:

• Multithreading: Allows multiple threads of execution within a single process, enabling concurrent tasks.
• Multiprocessing: Utilizes multiple processors to run different processes simultaneously, achieving true parallelism.

2. Synchronization and Concurrency Control:

Mechanisms to ensure data consistency and prevent conflicts when multiple threads or processes access shared resources.

6. Writing Your First Code

Here is your first code in different languages. These programs all achieve the same goal: printing “ Hello, world! ” to the console. However, they use different syntax and conventions specific to each language.

Printing “Hello world” in C++:

Explanation of above C++ code:

• #include: This keyword includes the <iostream> library, which provides functions for input and output.
• int main(): This defines the main function, which is the entry point of the program.
• std::cout <<: This keyword prints the following expression to the console.
• “Hello, world!” This is the string that is printed to the console.
• std::endl: This keyword inserts a newline character after the printed string.
• return 0; This statement exits the program and returns a success code (0).

Printing “Hello world” in Java:

Explanation of above Java code:

• public class HelloWorld: This keyword defines a public class named HelloWorld .
• public static void main(String[] args): This declares the main function, which is the entry point of the program.
• System.out.println(“Hello, world!”); This statement prints the string “ Hello, world! ” to the console.

Printing “Hello world” in Python:

Explanation of above Python code:

• print: This keyword prints the following argument to the console.

Printing “Hello world” in Javascript:

Explanation of above Javascript code:

• console.log: This object’s method prints the following argument to the console.

Printing “Hello world” in PHP:

Explanation of above PHP code:

• <?php: This tag initiates a PHP code block.
• echo: This keyword prints the following expression to the console.
• ?>: This tag ends the PHP code block.

7. Top 20 Programs to get started with Coding/Programming Tutorial:

Here are the list of some basic problem, these problems cover various fundamental programming concepts. Solving them will help you improve your coding skills and understanding of programming fundamentals.

8. Next Steps after learning basic Coding/Programming Tutorial:

Congratulations on taking the first step into the exciting world of programming! You’ve learned the foundational concepts and are ready to explore more. Here’s a comprehensive guide to help you navigate your next steps:

A. Deepen your understanding of Basic Programming Concepts:

• Practice regularly: Implement what you learn through practice problems and coding exercises.
• Solve code challenges: Platforms like GeeksforGeeks, HackerRank, LeetCode, and Codewars offer challenges to improve your problem-solving skills and coding speed.

B. Learn advanced concepts:

• Data structures: Learn about arrays, linked lists, stacks, queues, trees, and graphs for efficient data organization.
• Algorithms: Explore algorithms for searching, sorting, dynamic programming, and graph traversal.
• Databases: Learn SQL and NoSQL databases for data storage and retrieval.
• Version control: Use Git and GitHub for code versioning and collaboration.

C. Choose a focus area:

• Web development: Learn HTML, CSS, and JavaScript to build interactive web pages and applications.
• Mobile app development: Choose frameworks like Flutter (Dart) or React Native (JavaScript) to build cross-platform apps.
• Data science and machine learning: Explore Python libraries like NumPy, pandas, and scikit-learn to analyze data and build machine learning models.
• Game development: Learn game engines like Unity (C#) or Unreal Engine (C++) to create engaging games.
• Desktop app development: Explore frameworks like PyQt (Python) or C# to build desktop applications.
• Other areas: Explore other areas like robotics, embedded systems, cybersecurity, or blockchain development based on your interests.

D. Build projects:

• Start with small projects: Begin with simple projects to apply your knowledge and gain confidence.
• Gradually increase complexity: As you progress, tackle more challenging projects that push your boundaries.
• Contribute to open-source projects: Contributing to open-source projects is a great way to learn from experienced developers and gain valuable experience.
• Showcase your work: Create a portfolio website or blog to showcase your skills and projects to potential employers or clients.

9. Resources and Further Learning

A. Online Courses and Tutorials:

• Interactive platforms: GeeksforGeeks, Codecademy, Coursera, edX, Khan Academy
• Video tutorials: GeeksforGeeks, YouTube channels like FreeCodeCamp, The Coding Train, CS50’s Introduction to Computer Science
• Language-specific tutorials: GeeksforGeeks, Official documentation websites, blogs, and community-driven resources

B. Books and eBooks:

• Beginner-friendly books: “Python Crash Course” by Eric Matthes, “Head First Programming” by David Griffiths
• A dvanced topics: “Clean Code” by Robert C. Martin, “The Pragmatic Programmer” by Andrew Hunt and David Thomas
• Free ebooks: Many free programming ebooks are available online, such as those on Project Gutenberg

C. Programming Communities and Forums:

• Stack Overflow: Q&A forum for programming questions
• GitHub: Open-source platform for hosting and collaborating on code projects
• Reddit communities: r/learnprogramming, r/python, r/webdev
• Discord servers: Many languages have dedicated Discord servers for discussions and support

D. Tips for Staying Motivated and Learning Effectively:

• Set realistic goals and deadlines.
• Start small and gradually increase complexity.
• Practice regularly and code consistently.
• Find a learning buddy or group for accountability.
• Participate in online communities and forums.
• Take breaks and avoid burnout.
• Most importantly, have fun and enjoy the process

10. Frequently Asked Questions (FAQs) on Programming Tutorial:

Question 1: how to learn programming without tutorial.

Answer: Learning programming without tutorials involves a self-directed approach. Start by understanding fundamental concepts, practicing regularly, and working on small projects. Utilize books, documentation, and online resources for reference.

Question 2: How to learn coding tutorial?

Answer: Learning coding through tutorials involves choosing a programming language, finding online tutorials or courses, and following them step by step. Practice coding alongside the tutorial examples and apply the concepts to real-world projects for a hands-on learning experience.

Question 3: What are 3 important things to know about programming?

Answer: Problem Solving: Programming is fundamentally about solving problems. Logic and Algorithms: Understanding logical thinking and creating efficient algorithms is crucial. Practice: Regular practice and hands-on coding improve skills and understanding.

Question 4: How many days do I need to learn programming?

Answer: The time to learn programming varies based on factors like prior experience, the complexity of the language, and the depth of knowledge desired. Learning the basics can take weeks, but mastery requires continuous practice over months.

Question 5: Can tutorials help coding?

Answer: Yes, tutorials are valuable resources for learning coding. They provide structured guidance, examples, and explanations, making it easier to understand and apply Programming Tutorial concepts.

Question 6: How do you use tutorials effectively?

Answer: Use tutorials effectively by following these steps: Set clear learning goals. Work on hands-on exercises and projects. Seek additional resources for deeper understanding. Regularly review and practice concepts learned.

Question 7: Can coding be done on a phone?

Answer: Yes, coding can be done on a phone using coding apps or online platforms that provide mobile-friendly coding environments. However, a computer is generally more practical for extensive coding tasks.

Question 8: Can I learn coding on GeeksforGeeks?

Answer: Yes, GeeksforGeeks is a popular platform for learning coding. Many Tutorials, Courses are provided you to learn various programming languages and concepts.

Question 9: Can we do coding on a laptop?

Answer: Yes, coding can be done on a laptop. Laptops are common tools for coding as they provide a portable and versatile environment for writing, testing, and running code.

Question 10: What is the difference between coding and programming?

Answer: The terms are often used interchangeably, but coding is typically seen as the act of writing code, while programming involves a broader process that includes problem-solving, designing algorithms, and implementing solutions. Programming encompasses coding as one of its stages.

This comprehensive programming tutorial has covered the fundamentals you need to start coding. Stay updated with emerging technologies and keep practicing to achieve your goals. Remember, everyone starts as a beginner. With dedication, you can unlock the world of programming!

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Solving Basic Programming Problems is the key to achieve success in coding challenges. Students must practice these basic programming problems!

Basic Programming Problems: Engaging in code challenges offers many benefits, serving as a dynamic tool to enhance problem-solving proficiency, deepen your comprehension of the programming language you work with, and acquaint yourself with diverse algorithms. If you aspire to elevate your programming skills, immersing yourself in coding is the most effective avenue.

The beauty of basic programming problems lies in their convenience—they provide a platform to hone your abilities through bite-sized problems, often eliminating the need to construct entire applications. This characteristic allows you to conquer these challenges swiftly, fostering a sense of accomplishment.

Moreover, code challenges are integral components of many coding interviews.

While your resume may showcase your skills and ability to articulate programming concepts, employers want to validate your practical coding capabilities. Tackling coding challenges during interviews becomes a testament to your proficiency and showcases your competence for the role.

Therefore, incorporating coding challenges into your routine sharpens your skills and is an invaluable preparation strategy for job interviews. To kickstart your coding journey, we have curated a collection of popular basic programming problems to pave the way for your continued growth.

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Basic Programming Problems Overview

Basic programming problems provide an essential foundation for individuals learning to code, offering a practical and hands-on approach to mastering fundamental concepts in programming.

These problems are designed to introduce beginners to the core coding principles, gradually building their problem-solving skills and comprehension of programming logic.

Whether you are a novice looking to embark on your coding journey or an experienced programmer aiming to reinforce your foundational knowledge, engaging with basic programming problems is a valuable practice.

These problems typically cover essential topics such as data types, loops, conditionals, functions, and basic algorithms, providing a well-rounded introduction to the key building blocks of programming.

The significance of basic programming problems extends beyond mere skill development; it serves as a stepping stone for individuals aspiring to pursue more advanced coding challenges and projects.

By grappling with these foundational problems, learners can cultivate a solid understanding of programming fundamentals, laying the groundwork for future exploration and mastery of more complex coding concepts. Basic programming problems are the cornerstone of a programmer’s educational journey, fostering a strong and resilient coding skill set.

Basic Programming Problems for Beginners

Starting your career in the programming field is  exciting and challenging. For beginners, mastering the basics is crucial, and what better way to do so than by solving basic programming problems ?

Basic Programming Problems Java

Here are some of the basic programming problems JAVA :

1) Hello World:

public class HelloWorld {

    public static void main(String[] args) {

        System.out.println(“Hello, World!”);

2) The sum of Two Numbers:

Add two numbers and print the result.

public class Sum {

        int num1 = 5, num2 = 10, sum;

        sum = num1 + num2;

        System.out.println(“Sum: ” + sum);

3) Factorial of a Number:

Calculate the factorial of a number.

public class Factorial {

        int num = 5;

        long factorial = 1;

        for (int i = 1; i <= num; ++i) {

            factorial *= i;

        System.out.println(“Factorial: ” + factorial);

4) Check Even or Odd:

Determine if a number is even or odd.

public class EvenOdd {

        int num = 8;

        if (num % 2 == 0) {

            System.out.println(num + ” is even.”);

        } else {

            System.out.println(num + ” is odd.”);

5) Reverse a String:

Reverse the characters in a given string.

public class ReverseString {

        String str = “Hello”;

        StringBuilder reversed = new StringBuilder(str).reverse();

        System.out.println(“Reversed String: ” + reversed);

Here are some theory-based basic programming problems Java:

1) Differences Between C++ and Java

• C++: Not platform-independent, follows “write once, compile anywhere.”
• Java: Platform-independent byte code allows programs to run on any machine.

Languages Compatibility:

• C++: Compatible with most high-level languages.
• Java: Incompatible with most languages, comparable to C and C++.

Interaction with the Library:

• C++: Direct access to native system libraries, suitable for system-level programming.
• Java: Requires Java Native Interface or library access, not direct call support.

Characteristics:

• C++: Combines features of procedural and object-oriented languages.
• Java: Known for automatic garbage collection, lacks support for destructors.

Semantics of the Type:

• C++: Consistent semantics for primitive and object types.
• Java: Inconsistent semantics between primitive and object types and classes.

Compiler and Interpreter:

• Java: Compiled and interpreted language, source code compiles into platform-independent bytecode.
• C++: Purely compiled language, source program compiles into object code, further executed.

2) Features of the Java Programming Language:

• Easy: Java is considered easy to learn, with fundamental Object-Oriented Programming (OOP) concepts.
• Secured Feature: Java provides a secured feature, ensuring the development of virus-free and tamper-free systems.
• OOP: Java follows Object-Oriented Programming, treating everything as an object.
• Independent Platform: Java compiles into platform-independent bytecode, interpreted by the Virtual Machine.

3) ClassLoader in Java:

• A ClassLoader in Java is a subsystem of the Java Virtual Machine responsible for loading class files during program execution.
• It is the first to load the executable file and includes Bootstrap, Extension, and Application classloaders.

4) Differences Between Heap and Stack Memory in Java:

• Stack Memory: Allocated to each individual program. Fixed memory space.
• Heap Memory: Not assigned to Java code initially but available during runtime. Used as needed by the Java code.

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Basic Programming Problems in C

The table below shows the basic programming problems in C :

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Basic Programming Problems in Python

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Basic Programming Problems in Javascript

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Basic Programming Problems and Solutions

Here are 10 basic programming problems along with their solutions:

• Hello World:

Problem: Write a program that prints “Hello, World!” to the console.

Solution (Python):

print(“Hello, World!”)

• Sum of Two Numbers:

Problem: Write a program that inputs two numbers and prints their sum.

Solution (Java):

import java.util.Scanner;

public class SumOfTwoNumbers {

        Scanner scanner = new Scanner(System.in);

        System.out.print(“Enter first number: “);

        int num1 = scanner.nextInt();

        System.out.print(“Enter second number: “);

        int num2 = scanner.nextInt();

        int sum = num1 + num2;

• Factorial of a Number:

Problem: Write a program to calculate the factorial of a given number.

Solution (C++):

#include <iostream>

using namespace std;

int factorial(int n) {

    if (n == 0 || n == 1)

        return 1;

        return n * factorial(n – 1);

int main() {

    int num;

    cout << “Enter a number: “;

    cin >> num;

    cout << “Factorial: ” << factorial(num) << endl;

    return 0;

• Check Even or Odd:

Problem: Write a program that checks if a given number is even or odd.

Solution (JavaScript):

let number = 7;

if (number % 2 === 0) {

    console.log(number + ” is even”);

    console.log(number + ” is odd”);

• Reverse a String:

Problem: Write a program to reverse a given string.

original_string = “Hello, World!”

reversed_string = original_string[::-1]

print(“Reversed String:”, reversed_string)

• Fibonacci Series:

Problem: Generate the Fibonacci series up to a specific limit.

public class FibonacciSeries {

        int limit = 10;

        int firstTerm = 0, secondTerm = 1;

        System.out.println(“Fibonacci Series up to ” + limit + ” terms:”);

        for (int i = 1; i <= limit; ++i) {

            System.out.print(firstTerm + “, “);

            int nextTerm = firstTerm + secondTerm;

            firstTerm = secondTerm;

            secondTerm = nextTerm;

• Check Prime Number:

Problem: Write a program to check if a given number is prime.

def is_prime(number):

    if number > 1:

        for i in range(2, int(number / 2) + 1):

            if (number % i) == 0:

                return False

        else:

            return True

        return False

if is_prime(num):

    print(num, “is a prime number.”)

    print(num, “is not a prime number.”)

• Find Maximum Element:

Problem: Write a program to find the maximum element in an array.

int findMax(int arr[], int size) {

    int max = arr[0];

    for (int i = 1; i < size; ++i) {

        if (arr[i] > max) {

            max = arr[i];

    return max;

    int numbers[] = {5, 8, 2, 10, 3};

    int size = sizeof(numbers) / sizeof(numbers[0]);

    cout << “Maximum Element: ” << findMax(numbers, size) << endl;

• Palindrome Check:

Problem: Write a program to check if a given string is a palindrome.

public class PalindromeCheck {

        String str = “level”;

        String reversedStr = new StringBuilder(str).reverse().toString();

        if (str.equals(reversedStr)) {

            System.out.println(str + ” is a palindrome.”);

            System.out.println(str + ” is not a palindrome.”);

• Count Vowels and Consonants:

Problem: Write a program to count the number of vowels and consonants in a given string.

text = “Hello, World!”

vowels = “AEIOU

Benefits of Solving Basic Programming Problems

Solving basic programming problems offers numerous benefits for individuals looking to enhance their programming skills. Here are some key advantages:

Skill Development:

• Coding Proficiency: Regular problem-solving helps improve your coding skills and fluency in programming languages.
• Algorithmic Thinking: It fosters the development of algorithmic thinking, enabling you to devise efficient solutions to various problems.

Logical Thinking:

• Problem Decomposition: Breaking down problems into smaller components and solving them enhances logical thinking and problem-solving abilities.
• Pattern Recognition: Regular problem-solving helps in recognizing patterns and similarities between different problems, leading to more efficient solutions.

Learning New Concepts:

• Exposure to Diverse Topics: Programming problems often cover a wide range of concepts, exposing you to different areas of computer science and software development.
• New Algorithms and Data Structures: Exploring various problems introduces you to new algorithms and data structures, expanding your knowledge base.

Preparation for Interviews:

• Technical Interviews: Many technical interviews for programming roles involve solving algorithmic and coding problems. Regular practice prepares you for such interviews and boosts your confidence.
• Coding Challenges: Familiarity with common coding challenges often encountered in interviews is an asset.

Building a Portfolio:

• Showcasing Skills: Solving problems allows you to build a portfolio of solutions that you can showcase to potential employers or on coding platforms.
• GitHub Contributions: Uploading your solutions to platforms like GitHub demonstrates your coding proficiency and problem-solving ability.

Enhanced Efficiency:

• Code Optimization: Regular practice encourages optimization, leading to more efficient and cleaner code.
• Time Complexity Awareness: Problem-solving helps in understanding and considering time complexity, contributing to the creation of scalable solutions.

Community Engagement:

• Online Communities: Engaging in online coding communities allows you to discuss problems, learn from others, and gain insights into alternative solutions.
• Peer Learning: Collaborating with peers on coding challenges can provide different perspectives and foster a collaborative learning environment.

Career Advancement:

• Competitive Edge: Building strong problem-solving skills sets you apart in a competitive job market, enhancing your employability.
• Adaptability: A wide range of problem-solving experiences makes you more adaptable to different tasks and projects.

Personal Satisfaction:

• Sense of Achievement: Successfully solving programming problems brings a sense of accomplishment, boosting confidence and motivation.
• Continuous Learning: It fosters a mindset of continuous learning, crucial in a rapidly evolving field like programming.

In summary, regular practice of solving basic programming problems contributes significantly to skill development, logical thinking, and overall proficiency in the field of programming.

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The 10 Most Popular Coding Challenge Websites [Updated for 2021]

A great way to improve your skills when learning to code is by solving coding challenges. Solving different types of challenges and puzzles can help you become a better problem solver, learn the intricacies of a programming language, prepare for job interviews, learn new algorithms, and more.

Below is a list of some popular coding challenge websites with a short description of what each one offers.

1. TopCoder

TopCoder is one of the original platforms for competitive programming online. It provides a list of algorithmic challenges from the past that you can complete on your own directly online using their code editor. Their popular Single Round Matches are offered a few times per month at a specific time where you compete against others to solve challenges the fastest with the best score.

The top ranked users on TopCoder are very good competitive programmers and regularly compete in programming competitions. The top ranked user maintains his own blog titled Algorithms weekly by Petr Mitrichev where he writes about coding competitions, algorithms, math, and more.

2. Coderbyte

Coderbyte provides 200+ coding challenges you can solve directly online in one of 10 programming languages (check out this example ). The challenges range from easy (finding the largest word in a string) to hard (print the maximum cardinality matching of a graph).

They also provide a collection of algorithm tutorials , introductory videos, and interview preparation courses . Unlike HackerRank and other similar websites, you are able to view the solutions other users provide for any challenge aside from the official solutions posted by Coderbyte.

3. Project Euler

Project Euler provides a large collection of challenges in the domain of computer science and mathematics. The challenges typically involve writing a small program to figure out the solution to a clever mathematical formula or equation, such as finding the sum of digits of all numbers preceding each number in a series.

You cannot directly code on the website in an editor, so you would need to write a solution on your own computer and then provide the solution on their website.

4. HackerRank

HackerRank provides challenges for several different domains such as Algorithms, Mathematics, SQL, Functional Programming, AI, and more. You can solve all the challenge directly online (check out this example ).

They provide a discussion and leaderboard for every challenge, and most challenges come with an editorial that explains more about the challenge and how to approach it to come up with a solution.

Currently, if you don't solve the problem, then you can't see the solution of others. If you also try to check the editorial before solving the problem, then you won't get the point for solving the problem at all.

As an example, here I haven't solved the problem, and I am trying to check others' submissions:

And here, I haven't solved the problem, and I am trying to check the editorial:

HackerRank also provides the ability for users to submit applications and apply to jobs by solving company-sponsored coding challenges.

5. CodeChef

CodeChef is an Indian-based competitive programming website that provides hundreds of challenges. You are able to write code in their online editor and view a collections of challenges that are separated into different categories depending on your skill level (check out this example ). They have a large community of coders that contribute to the forums, write tutorials , and take part in CodeChef’s coding competitions .

6. Exercism.io

Exercism is a coding challenge website that offers 3100+ challenges spanning 52 different programming languages. After picking a language that you'd like to master, you tackle the coding challenges right on your machine (Exercism has their own command line interface that you can download from GitHub).

It is a bit different from other challenge websites, however, because you work with a mentor after completing each challenge. The mentor reviews your answers online and helps you improve them if needed. Once your answers have been approved and submitted, you unlock more challenges.

7. Codewars

Codewars provides a large collection of coding challenges submitted and edited by their own community. You can solve the challenges directly online in their editor in one of several languages. You can view a discussion for each challenges as well as user solutions.

8. LeetCode

LeetCode is a popular Online Judge that provides a list of 190+ challenges that can help you prepare for technical job interviews. You can solve the challenges directly online in one of 9 programming languages. You are not able to view other users' solutions, but you are provided statistics for your own solutions such as how fast your code ran when compared to other users' code.

They also have a Mock Interview section that is specifically for job interview preparation, they host their own coding contests , and they have a section for articles to help you better understand certain problems.

Sphere Online Judge (SPOJ) is an online judge that provides over 20k coding challenges. You are able to submit your code in an online editor . SPOJ also hosts their own contests and has an area for users to discuss coding challenges. They do not currently provide any official solutions or editorials like some other websites do, though.

10. CodinGame

CodinGame is a bit different from the other websites, because instead of simply solving coding challenges in an editor, you actually take part in writing the code for games that you play directly online. You can see a list of games currently offered here and an example of one here . The game comes with a problem description, test cases, and an editor where you can write your code in one of 20+ programming languages.

Although this website is different than typical competitive programming websites such as the ones mentioned above, it is still popular amongst programmers who enjoy solving challenges and taking part in contests.

This list was based on a few things: my own experiences using the websites, some Google searches , Quora posts , and articles such as this one and this one . I also frequented some forums and subreddits such as r/learnprogramming to see what websites were usually recommended by the users there. Disclaimer: I work at Coderbyte which is one of the websites mentioned above.

CEO & Founder at Coderbyte.

If this article was helpful, share it .

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Student Value Edition for Java: An Introduction to Problem Solving and Programming (6th Edition) 6th Edition

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For introductory Computer Science courses using Java, and other introductory programming courses in departments of Computer Science, Computer Engineering, CIS, MIS, IT, and Business.

Students are introduced to object-oriented programming and important concepts such as design, testing and debugging, programming style, interfaces inheritance, and exception handling. The Java coverage is a concise, accessible introduction that covers key language features. Objects are covered thoroughly and early in the text, with an emphasis on application programs over applets.

Updated for Java 7, the Sixth Edition contains additional programming projects, case studies, and VideoNotes.

MyProgrammingLab, Pearson's new online homework and assessment tool, is available with this edition. Subscriptions to MyProgrammingLab are available to purchase online or packaged with your textbook (unique ISBN). Use the following ISBNs to purchase MyProgrammingLab:

• Student Value Edition for Java: An Introduction to Problem Solving and Programing, 6e, & MyProgrammingLab with Pearson eText Student access code card for J ava: An Introduction to Problem Solving and Programing, 6e ISBN: 013276606X This package contains the Student Value Edition for Java: An Introduction to Problem Solving and Programing, 6e, textbook, an access card for MyProgrammingLab, and the Pearson eText student access code card for Java: An Introduction to Problem Solving and Programing, 6e.
• Purchase instant access to MyProgrammingLab online.
• ISBN-10 0132773333
• ISBN-13 978-0132773331
• Edition 6th
• Publisher Pearson
• Publication date May 26, 2011
• Language English
• Dimensions 7.24 x 1.34 x 9.06 inches
• Print length 936 pages
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• Publisher ‏ : ‎ Pearson; 6th edition (May 26, 2011)
• Language ‏ : ‎ English
• Loose Leaf ‏ : ‎ 936 pages
• ISBN-10 ‏ : ‎ 0132773333
• ISBN-13 ‏ : ‎ 978-0132773331
• Item Weight ‏ : ‎ 2.5 pounds
• Dimensions ‏ : ‎ 7.24 x 1.34 x 9.06 inches
• #512 in Beginner's Guides to Java Programming
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• #8,333 in Introductory & Beginning Programming

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Robotics for Kids: The Future With AI and Robotics Education

R obotics for kids is an exciting and engaging way to introduce children to the world of technology, engineering, and programming. By getting into this interactive field, kids can develop essential skills such as creativity, problem-solving, and coding. It’s never too early for kids to explore their curiosity for robotics, as various robotic toys and kits cater to different ages and interests.

Science, technology, engineering, and math (STEM) focused and clever robotic toys help kids understand the science behind circuits and motion and inspire them to take on more challenging projects in the future. 

The Foundations of Robotics

When teaching kids about robotics, understanding the basics is the first part. Robotics is an exciting field combining STEM elements.

Let’s start with the core components that make up robotics:

• Mechanical Engineering : This aspect deals with robot design, construction, and operation. It involves understanding materials, structures, and mechanisms that enable robots to perform various tasks, such as moving, grabbing, and interacting with objects.
• Electrical Engineering : In this area, kids learn about electronic circuits and components that power robotic systems. They’ll explore topics such as voltage, current, and resistance, helping them grasp how electrical signals control a robot’s movements and functions.
• Computer Programming : At the heart of a robot lies its ‘brain’ – a computer or microcontroller that processes information and sends commands to its mechanical and electrical components. Kids can create code that brings a robot to life by learning programming languages like Scratch or Python.

With these components covered, kids can create robots, experimenting with other designs and functions. 

Getting Started With Robotics

For beginners, it’s good to start with a simple design and building projects, like creating a bristlebot, a tiny robot made from a toothbrush head, a battery, and a small motor. This simple project introduces the basic concepts of robot design and encourages hands-on learning.

When explaining robotics to kids, maintain a casual tone and keep explanations relatable and straightforward. Encourage creativity, problem-solving, and critical thinking as crucial skills for thriving in robotics.

Choosing a Starter Robotics Kit

Consider investing in a starter robotics kit to make the initial jump enjoyable. Here are some popular options for various age groups:

• LEGO Education WeDo 2.0
• Dash Robot by Wonder Workshop
• mBot by Makeblock
• LEGO Mindstorms EV3

These kits typically come with clear instructions, software, and all the necessary components to build a functional robot. They offer a learning experience with simple, well-guided projects to help kids familiarize themselves with coding, electronics, and mechanics.

Considerations and Challenges in Robotics for Kids

While robotics offers kids an exciting gateway into technology and innovation, it will have some challenges and other considerations. 

Firstly, the cost can be a significant factor; not all families can easily access expensive robotics kits or resources. It’s vital to explore cost-effective alternatives or seek out community programs that provide access to robotics education. Robot kits have a wide price range, from beginner to more complex, so there is a right fit for every family. 

Additionally, supervision is crucial, especially for younger children. Working with electronic components and programming requires guidance to ensure safety and practical learning. 

Moreover, the learning curve varies from child to child. Some may grasp concepts quickly, while others need more time and patience. Recognizing these challenges helps us approach robotics education for children in a more supportive way, ensuring that every child can explore and learn at their own pace.

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Digging deeper into robotics.

For kids, programming is vital in helping them understand how to give robots the intelligence and capabilities to perform the desired tasks. Kids can start by learning block-based programming languages like Scratch, which provide an easy way to grasp coding concepts without syntax complexity.

Children can explore more advanced programming languages like Python and Java as they progress. Python is a versatile language favored by many for its readability and ease of use. In contrast, Java, though more complex, is widely used in various industries and provides a solid foundation for further learning.

This is a list of programming languages that can be used in robotics:

• Scratch (block-based)

The Science Behind Robotics

Understanding science will help kids to comprehend robotics fully. The science behind robotics involves various fields, such as artificial intelligence (AI), machine learning, and mechanical engineering. By grasping these concepts, kids can further enhance their robotic creations and make them more advanced and efficient.

AI and machine learning, in particular, have been instrumental in making robots more adept at mimicking human behavior and learning from their environments. These technologies allow robots to analyze large data sets and draw new insights, enabling them to adapt and grow over time.

For instance, comparing AI, machine learning, and robotics can be as follows:

• Key Focus: Simulating human intelligence in machines
• Application in Robotics: Decision-making and pattern recognition
• Key Focus: Algorithms that learn from data and improve over time
• Application in Robotics: Autonomously adapting to new scenarios
• Key Focus: Design, construction, and operation of robots
• Application in Robotics: Physical implementation of AI and machine learning concepts

Taking Robotics to the Next Level

For kids who have already mastered the basics of robotics, various advanced projects can help them further deepen their understanding. By participating in more complex projects, they’ll enhance crucial skills like problem-solving, coding, and engineering .

Some advanced project ideas include:

• Walking robots : Designing a robot that can walk using servo motors, microcontrollers, and sensors
• Remote-controlled robots : Building a robot that can be controlled wirelessly through a smartphone or a custom remote control
• Autonomous robots : Creating a self-driving robot that can navigate using different sensors, like ultrasonic or infrared

These projects provide a great learning experience and allow kids to showcase their creations to the community by sharing videos, participating in online forums, and entering robotics competitions.

Robotics in Real-Life Applications

Robotics isn’t just a fun hobby – it’s a growing field with a wide range of real-life applications that have the potential to make an immense impact. Here are some examples of where robotics is playing a vital role:

• Self-driving cars : Companies like Tesla and Waymo are working on fully autonomous vehicles that rely on advanced robotics and artificial intelligence. Introducing kids to robotics can increase their interest in the technology powering these cars.
• Manufacturing : Robotics is transforming the manufacturing industry by automating tasks, improving efficiency, and reducing the risk of human injuries. Familiarizing kids with robotics can prepare them for career opportunities.
• NASA : Robotics is playing a significant role in space exploration, too. NASA uses robots like the Mars rovers to collect and analyze data from other planets, helping scientists learn more about our solar system.

Taking robotics to the next level helps kids hone their skills and exposes them to the vast possibilities of applying their knowledge to real-life scenarios. 

The Future of Robotics

In the coming years, we anticipate many breakthroughs in robotics and AI technology, leading to the development of more advanced and user-friendly robots. These innovations will significantly impact people’s everyday lives, including kids, who will grow up surrounded by technology and automation.

Children should be introduced to robotics at a young age or whenever they show interest in how things work. Encouraging kids to study topics related to robotics, AI, and technology will equip them with the necessary skills and spark their curiosity and creativity. 

Here are some aspects kids can focus on:

• Coding : Learning to code helps build a strong foundation for understanding and interacting with robots and AI systems
• STEM education : Science, Technology, Engineering, and Mathematics are essential fields that contribute to advancing robotics and AI
• Problem-solving : Developing critical thinking and problem-solving skills will enable kids to adapt to and understand the complexities of future technology

Integrating robots and AI will play a significant role in various sectors, such as education, healthcare, and transportation. 

For kids, this might translate into classrooms becoming more interactive with AI-driven teaching assistants that can customize learning experiences. Additionally, robotic toys could engage and stimulate creativity while teaching programming and other skills. There would also be a greater emphasis on technologically driven extracurricular activities, including robotic clubs and competitions.

As we wrap up, it’s clear that robotics isn’t just an excellent hobby for kids; it’s a window into a future filled with exciting possibilities. It’s more than just coding and gears; it sparks curiosity, builds problem-solving skills, and opens doors to creativity. 

There are hurdles like cost and the need for guidance, but these are small compared to the enormous benefits. By giving kids a head start in robotics, we’re not just keeping them busy with a fun activity. We’re preparing them for a world where technology is everywhere. And who knows? Today’s kids tinkering with robots could lead to the next big tech revolution.

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