how to be good at problem solving in programming

How to think like a programmer — lessons in problem solving

Richard Reis

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If you’re interested in programming, you may well have seen this quote before:

“Everyone in this country should learn to program a computer, because it teaches you to think.” — Steve Jobs

You probably also wondered what does it mean, exactly, to think like a programmer? And how do you do it??

Essentially, it’s all about a more effective way for problem solving .

In this post, my goal is to teach you that way.

By the end of it, you’ll know exactly what steps to take to be a better problem-solver.

Why is this important?

Problem solving is the meta-skill.

We all have problems. Big and small. How we deal with them is sometimes, well…pretty random.

Unless you have a system, this is probably how you “solve” problems (which is what I did when I started coding):

• Try a solution.
• If that doesn’t work, try another one.
• If that doesn’t work, repeat step 2 until you luck out.

Look, sometimes you luck out. But that is the worst way to solve problems! And it’s a huge, huge waste of time.

The best way involves a) having a framework and b) practicing it.

“Almost all employers prioritize problem-solving skills first. Problem-solving skills are almost unanimously the most important qualification that employers look for….more than programming languages proficiency, debugging, and system design. Demonstrating computational thinking or the ability to break down large, complex problems is just as valuable (if not more so) than the baseline technical skills required for a job.” — Hacker Rank ( 2018 Developer Skills Report )

Have a framework

To find the right framework, I followed the advice in Tim Ferriss’ book on learning, “ The 4-Hour Chef ”.

It led me to interview two really impressive people: C. Jordan Ball (ranked 1st or 2nd out of 65,000+ users on Coderbyte ), and V. Anton Spraul (author of the book “ Think Like a Programmer: An Introduction to Creative Problem Solving ”).

I asked them the same questions, and guess what? Their answers were pretty similar!

Soon, you too will know them.

Sidenote: this doesn’t mean they did everything the same way. Everyone is different. You’ll be different. But if you start with principles we all agree are good, you’ll get a lot further a lot quicker.

“The biggest mistake I see new programmers make is focusing on learning syntax instead of learning how to solve problems.” — V. Anton Spraul

So, what should you do when you encounter a new problem?

Here are the steps:

1. Understand

Know exactly what is being asked. Most hard problems are hard because you don’t understand them (hence why this is the first step).

How to know when you understand a problem? When you can explain it in plain English.

Do you remember being stuck on a problem, you start explaining it, and you instantly see holes in the logic you didn’t see before?

Most programmers know this feeling.

This is why you should write down your problem, doodle a diagram, or tell someone else about it (or thing… some people use a rubber duck ).

“If you can’t explain something in simple terms, you don’t understand it.” — Richard Feynman

Don’t dive right into solving without a plan (and somehow hope you can muddle your way through). Plan your solution!

Nothing can help you if you can’t write down the exact steps.

In programming, this means don’t start hacking straight away. Give your brain time to analyze the problem and process the information.

To get a good plan, answer this question:

“Given input X, what are the steps necessary to return output Y?”

Sidenote: Programmers have a great tool to help them with this… Comments!

Pay attention. This is the most important step of all.

Do not try to solve one big problem. You will cry.

Instead, break it into sub-problems. These sub-problems are much easier to solve.

Then, solve each sub-problem one by one. Begin with the simplest. Simplest means you know the answer (or are closer to that answer).

After that, simplest means this sub-problem being solved doesn’t depend on others being solved.

Once you solved every sub-problem, connect the dots.

Connecting all your “sub-solutions” will give you the solution to the original problem. Congratulations!

This technique is a cornerstone of problem-solving. Remember it (read this step again, if you must).

“If I could teach every beginning programmer one problem-solving skill, it would be the ‘reduce the problem technique.’ For example, suppose you’re a new programmer and you’re asked to write a program that reads ten numbers and figures out which number is the third highest. For a brand-new programmer, that can be a tough assignment, even though it only requires basic programming syntax. If you’re stuck, you should reduce the problem to something simpler. Instead of the third-highest number, what about finding the highest overall? Still too tough? What about finding the largest of just three numbers? Or the larger of two? Reduce the problem to the point where you know how to solve it and write the solution. Then expand the problem slightly and rewrite the solution to match, and keep going until you are back where you started.” — V. Anton Spraul

By now, you’re probably sitting there thinking “Hey Richard... That’s cool and all, but what if I’m stuck and can’t even solve a sub-problem??”

First off, take a deep breath. Second, that’s fair.

Don’t worry though, friend. This happens to everyone!

The difference is the best programmers/problem-solvers are more curious about bugs/errors than irritated.

In fact, here are three things to try when facing a whammy:

• Debug: Go step by step through your solution trying to find where you went wrong. Programmers call this debugging (in fact, this is all a debugger does).

“The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.”” — Andrew Singer

• Reassess: Take a step back. Look at the problem from another perspective. Is there anything that can be abstracted to a more general approach?

“Sometimes we get so lost in the details of a problem that we overlook general principles that would solve the problem at a more general level. […] The classic example of this, of course, is the summation of a long list of consecutive integers, 1 + 2 + 3 + … + n, which a very young Gauss quickly recognized was simply n(n+1)/2, thus avoiding the effort of having to do the addition.” — C. Jordan Ball

Sidenote: Another way of reassessing is starting anew. Delete everything and begin again with fresh eyes. I’m serious. You’ll be dumbfounded at how effective this is.

• Research: Ahh, good ol’ Google. You read that right. No matter what problem you have, someone has probably solved it. Find that person/ solution. In fact, do this even if you solved the problem! (You can learn a lot from other people’s solutions).

Caveat: Don’t look for a solution to the big problem. Only look for solutions to sub-problems. Why? Because unless you struggle (even a little bit), you won’t learn anything. If you don’t learn anything, you wasted your time.

Don’t expect to be great after just one week. If you want to be a good problem-solver, solve a lot of problems!

Practice. Practice. Practice. It’ll only be a matter of time before you recognize that “this problem could easily be solved with <insert concept here>.”

How to practice? There are options out the wazoo!

Chess puzzles, math problems, Sudoku, Go, Monopoly, video-games, cryptokitties, bla… bla… bla….

In fact, a common pattern amongst successful people is their habit of practicing “micro problem-solving.” For example, Peter Thiel plays chess, and Elon Musk plays video-games.

“Byron Reeves said ‘If you want to see what business leadership may look like in three to five years, look at what’s happening in online games.’ Fast-forward to today. Elon [Musk], Reid [Hoffman], Mark Zuckerberg and many others say that games have been foundational to their success in building their companies.” — Mary Meeker ( 2017 internet trends report )

Does this mean you should just play video-games? Not at all.

But what are video-games all about? That’s right, problem-solving!

So, what you should do is find an outlet to practice. Something that allows you to solve many micro-problems (ideally, something you enjoy).

For example, I enjoy coding challenges. Every day, I try to solve at least one challenge (usually on Coderbyte ).

Like I said, all problems share similar patterns.

That’s all folks!

Now, you know better what it means to “think like a programmer.”

You also know that problem-solving is an incredible skill to cultivate (the meta-skill).

As if that wasn’t enough, notice how you also know what to do to practice your problem-solving skills!

Phew… Pretty cool right?

Finally, I wish you encounter many problems.

You read that right. At least now you know how to solve them! (also, you’ll learn that with every solution, you improve).

“Just when you think you’ve successfully navigated one obstacle, another emerges. But that’s what keeps life interesting.[…] Life is a process of breaking through these impediments — a series of fortified lines that we must break through. Each time, you’ll learn something. Each time, you’ll develop strength, wisdom, and perspective. Each time, a little more of the competition falls away. Until all that is left is you: the best version of you.” — Ryan Holiday ( The Obstacle is the Way )

Now, go solve some problems!

And best of luck 🙂

Special thanks to C. Jordan Ball and V. Anton Spraul . All the good advice here came from them.

Also, all the programming knowledge I’ve acquired in such a short time wouldn’t have happened without Lambda School . Can’t thank/ recommend them enough.

Bonus: Read this article in German .

Written by Richard Reis

"I write this not for the many, but for you; each of us is enough of an audience for the other." - Epicurus https://www.richardreis.me/

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How to Think like a Programmer When Problem Solving

How do programmers tackle problem-solving let’s take a look “under the hood”..

Christopher McFadden

Muhammad Baja Aksha/Flickr

Do you want to learn to problem-solve like a computer programmer? Then you have come to the right place.

Here we explore how the average programmer tackles problems and suggest ways to help you do the very same. A better, more efficient problem-solving future awaits you. 

At least in theory. 

RELATED: 9 OF THE FUNNIEST PROGRAMMER MEMES RIGHT NOW

What is problem-solving in programming?

Before we begin, it might be worth nailing out what problem-solving tends to mean for programmers. 

According to the School of Computing at the University of Utah , it is often defined as:

“Problem-solving [is] solving problems is the core of computer science. Programmers must first understand how a human solves a problem , then understand how to translate this ‘algorithm’ into something a computer can do, and finally how to ‘write’ the specific syntax (required by a computer) to get the job done.”

Sounds simple on the surface, but, as you are about to find out, it is actually something of an “art” more so than a science (to a certain extent).

Hold on tight…

How do you think like a programmer?

Steve Jobs famously said that “everyone in this country should learn to program a computer because it teaches you to think”. This is a very powerful message and doesn’t necessarily mean you need to become the next Ada Lovelace , Steve Wozniak , or Bill Gates .

Developing “soft skills” or “meta-skills” like learning how to think clearly and critically will pay dividends in many aspects of your life.

Especially in your professional career , whichever route you have chosen to take, be it an engineer or not. 

Along with other important “ meta-skills ” like perspective-taking, pain-spotting, story-telling, interviewing, and connecting the dots, mastering this skill is widely considered essential in our technologically complex world. In fact, many employers prioritize problem-solving, and basic coding to a certain extent, over most other skills when looking for potential new hires. 

“Problem-solving skills are almost unanimously the most important qualification that employers look for….more than programming languages proficiency, debugging, and system design.

Demonstrating computational thinking or the ability to break down large, complex problems is just as valuable (if not more so) than the baseline technical skills required for a job.” — Hacker Rank . 

So how can you develop such a skill? Well, one way is to attempt to think like a programmer. 

If you know any in your friend group your first port of call could be to have a frank and open discussion on this. It may feel awkward in the beginning, but you will quickly learn and love the process.

But, essentially, programmers learn, usually through trial and error, to become progressively more effective at problem-solving. Most people, unless you have developed a robust system, will likely involve something like the following: 

1. Attempt a solution.

2. If it doesn’t work, try another one.

3. If that also fails, rinse and repeat step 2 until you find something that works (perhaps through luck).

While it sounds like a reasonable approach, this methodology is both taxing on your mental energy and, most importantly, valuable and irreplaceable time (your only real asset in life). This is, in effect, the brute force method of solving problems. 

Programmers are masters of a more subtle and effective way of doing this through a plan, do, check, act strategy. This involves, in effect: 

1. Having a framework approach to tackling a problem.

2. Practice, learn from mistakes, review, and improve until you master it.

While, initially, this takes a big investment of your time, once you are well versed in it, it will pay dividends in the future.  You should also attempt to nurture some other life-style habits (like those of Elon Musk ) that will stand you in good stead when problem-solving using a programmer mindset.

How can I improve my programming problem-solving skills and develop a programmer mindset?

As we have previously mentioned, the best way is to develop a framework to problem-solve and then practice, practice, practice. But what does it mean to have a framework for problem-solving?

There is a vast library on just this subject, but in essence, a problem-solving framework will have some, if not all, of the following main components:

1. Break down an understand the issue at hand

The first step in your problem-solving framework should be to attempt to know exactly what is being asked, or required. Many, apparently insurmountable, problems are just so because we do not initially understand them in totality. 

So, how do you do this? One indicator that you’ve grasped the issue is by being able to explain it in plain English, or even a diagram. 

By thinking, and vocalizing, the problem you will quickly find the holes in logic, or your own understanding that you might have missed beforehand. This is one process that most programmers will be keenly aware of. 

Write down the problem, have a crack at sketching it out (perhaps using a mind map ), and try to explain it to someone else. 

2. Failing to plan, is planning to fail (make a plan and break down a large problem into smaller ones)

The next step is to try to formulate a plan of attack. Like anything in life, never start something until you’ve done this (especially things like investing your money ). 

Break down larger problems into smaller, more digestible chunks and think of ways to tick them off the list. You probably developed something similar to this during your time studying at university or any other time in your life. 

For programmers, they do not dive straight in without getting this phase sorted. The time invested at this stage will save you a ton of wasted time later down the road.

This is the programmer mindset.

Try to answer a question like, “given an input, what are the steps I need to return another output?”, etc. Not to mention a network of “ if this then that , or something else” set of instructions.

This is critical for programmers as they need to develop subroutines, functions,  methods, and procedures, and other smaller bits of code, that must work as a whole.

An error in one can, and usually will, impact other parts of the code and can even lead to issues like infinite loops . By working through each stage of the main problem in this way, errors and solutions can be tackled as and when they crop up.

It is also not unusual for a hang-up to require problem-solving in multiple smaller self-contained chunks of code too. This is, in essence, a way of connecting the dots.

On a side note, this is also one of the main reasons comments are used in code by programmers. Especially when working as part of a team (no one can read another’s mind).

But, we digress.

You should not be afraid to ask for help, or so some research. You may not have to reinvent the wheel. But DO NOT rely on other’s solutions to your problems as you won’t learn and develop your own mind and skill in a particular area.

“ If I could teach every beginning programmer one problem-solving skill, it would be the ‘reduce the problem technique’…

Reduce the problem to the point where you know how to solve it and write the solution. Then expand the problem slightly and rewrite the solution to match, and keep going until you are back where you started.” – V. Anton Spraul.

3. “Stay on target”, you can and will get stuck at times

“The best-laid plans of mice and men  often go awry,” as Robert Burns famously said. Don’t worry, this is to be expected, and often embraced.

But, what do you do when you can’t solve a problem , especially when programming?

While many will throw up their hands at the first sight of a problem, or solution that fails, most programmers have developed an attitude of curiosity towards “ bugs “.

Not to mention a determination to fix them.

When they crop up, programmers will usually initiate a process, a mental function if you will, of the following:

• Engage in a debugging process by going step by step through their written solution to find obvious problems (like misuse of syntax). 
• If that fails, reassess by taking a step back and try to “find another angle” to the problem. Can anything be abstracted or are you missing something? In other words are you “missing the forest for the trees”? It is very, very common to get so bogged down in the detail that you often miss the bigger picture.
• If all fails, you can simply start all over again — you’d be amazed at what solutions can be found.

4. Practice makes perfect

A little cliche? Perhaps, but you can’t expect to solve all complex problem or sub-problem, overnight. Being good at anything takes time to practice, fail, try again, and master. 

As you build up your experience in problem-solving, the process will become quicker and quicker as you identify methods that you have used previously that can be applied in a given situation. In fact, many of the most successful people regularly partake in “mini-problem-solving” regularly. 

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Some play chess, others sudoku, yet others crosswords or even computer games, on a regular basis. Try to find one that you enjoy and work that grey matter!

And, probably most importantly of all, do not fear failure. You will learn far more from where you mess up than you ever do from where you succeed.  Learn to fail , and fail fast! 

And that’s a wrap. Your path to problem-solving as a programmer awaits!

Good luck traveler. 

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Christopher McFadden Christopher graduated from Cardiff University in 2004 with a Masters Degree in Geology. Since then, he has worked exclusively within the Built Environment, Occupational Health and Safety and Environmental Consultancy industries. He is a qualified and accredited Energy Consultant, Green Deal Assessor and Practitioner member of IEMA. Chris&rsquo;s main interests range from Science and Engineering, Military and Ancient History to Politics and Philosophy.

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Problem-Solving. How to Boost Your Ability to Solve Programming Tasks and Challenges

8 Steps to Improve Your Problem-Solving Skills as a Rookie Programmer

1. make sure you understand the problem, 2. break down the problem into smaller ones, 3. plan the solution first, 4. solve programming problems on various preparation platforms.

One of the most popular tech interview platforms with a huge community and over 1650 problems for you to practice. Supports 14 programming languages including Java.

Interview Cake

Another well-known website with all kinds of content for programmers, including programming tasks, articles, tips and lots of interview questions.

HackerEarth

Besides programming problems, this platform allows you to test yourself in mock interviews, as well as to participate in coding competitions and hackathons.

5. Use CodeGym to practice and learn how to approach programming problems

6. play coding games to practice problem-solving while having fun, 7. extend your knowledge of design patterns, algorithms, and data structures, 8. get feedback, 4 major applied programming techniques for problem solving, 1. debugging.

2. Code Refactoring

3. using data structures & algorithms.

4. Using Version Control Systems

Expert advice

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Hands-on Tutorial: How To Improve Your Problem-Solving Skills As A Programmer

The technical part is the easiest.

Kurtis Pykes

Towards Data Science

Programming is ultimately problem-solving. We only apply the programming language to express how we’ve thought about a problem and the approach we’re using to solve it.

The worst thing you could do is to start chipping away at the problem once it’s presented. This is where most newbie programmers get stuck and give up. Think back to school days when you had to do written assessments. Usually, the paper [or invigilator] would tell you how much time you have for planning and how much should be set aside for writing. It’s the same thing with programming.

Every problem is a coat for a series of smaller problems that have to be tackled. The technical part is always the easiest once you’ve clearly devised a clear plan of attack for how to solve the problem.

“ If I only had an hour to chop down a tree, I would spend the first 45 minutes sharpening my axe. ” — Abraham Lincoln.

To demonstrate this we’re going to work through a coding problem together. Coding problems are a good way to develop your ability to think through programs. The more exposure you get to…

Written by Kurtis Pykes

SaaS Content Writer for AI companies | Sharing everything I learn as I build my solo business to $10M in revenue

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How To Solve Any Problem as a Software Engineer

A deep dive into ways to find answers.

Laurent Zuijdwijk

Better Programming

Solving complex problems is one of the main attractions of software engineering for me. The combination of creativity and critical, logical thinking keeps me sharp and engaged in my work. Maybe it is the same for you. I love to dig into a problem and develop an elegant solution to a complex problem. The process of analysing a problem in a domain I am not familiar with to delivering a smart solution is beautiful to me. For me, it is the essence of being an engineer.

Here I will explore the problem-solving process. I will describe the steps we need to follow to become better and more efficient engineers. You will also find some suggestions to become better at this vital skill.

Get To Know the Problem Domain

When dealing with a new type of problem, we could dive in head first and try to force a solution. Sometimes, it even works. While it is good to avoid analysis paralysis, it is often better to take a step back and learn more about the issue at hand. Do you really understand the problem? There is a near 100% chance that the same work has been done before and people have solved the problem. Do we know where to look for these solutions? Does our problem consist of multiple components that have been solved…

Written by Laurent Zuijdwijk

Staff engineer at Doxy.me | Follow me for software engineering, performance and architecture content. Find me at https://tinyurl.com/32em29kr

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10 Steps to Solving a Programming Problem

Tips for new developers staring at a blank screen, unsure of where to start.

Valinda Chan

Some of the feedback I hear from new developers working on a programming problem revolves around uncertainty of where to start. You understand the problem, the logic, basics of the syntax, etc. If you see someone else’s code or have someone to guide you, you can follow along. But maybe you feel uncertain about doing it yourself and have trouble turning your thoughts into code at first even though you understand the syntax or logic. Here’s my process and some tips to tackling a sample problem that hopefully some of you may find helpful in your journey.

1. Read the problem at least three times (or however many makes you feel comfortable)

You can’t solve a problem you don’t understand. There is a difference between the problem and the problem you think you are solving. It’s easy to start reading the first few lines in a problem and assume the rest of it because it’s similar to something you’ve seen in the past. If you are making even a popular game like Hangman, be sure to read through any rules even if you’ve played it before. I once was asked to make a game like Hangman that I realized was “Evil Hangman” only after I read through the…

Written by Valinda Chan

Product & UX Design

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Lesson 27 of 33 By Hemant Deshpande

Table of Contents

Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future. 

Want a Top Software Development Job? Start Here!

Topics covered in this problem solving in programming article are:

• What is Problem Solving in Programming? 
• Problem Solving skills in Programming
• How does it impact your career ?
• Steps involved in Problem Solving
• Steps to improve Problem Solving in programming

What is Problem Solving in Programming?

Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.

How Does It Impact Your Career?

Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified. 

People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment. 

The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.

Problem Solving Skills in Programming

Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.

The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.

Steps Involved in Problem Solving

Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.

Basically, they are divided into four categories:

• Analysing the problem
• Developing the algorithm
• Testing and debugging

Analysing the Problem

Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily. 

Developing the Algorithm

It is required to decide a solution before writing a program. The procedure of representing the solution  in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.

Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code. 

Testing and Debugging

The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.

Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.

The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.

Steps to Improve Problem Solving in Programming

Right mindset.

The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.

Making Right Decisions

When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.

Keeping Ideas on Track

Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.

Learning from Feedbacks

A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.

Asking Questions

Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.

These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.

• Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
• These problem solving skills also help to develop more skills in a person and build a promising career.
• These skills also help to find the solutions for critical and complex problems in a perfect way.
• Learning and developing problem solving in programming helps in building a good foundation.
• Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities 

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Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.

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About the Author

Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.

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20 Code Challenges To Put What You’re Learning to the Test

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Code challenges help you build problem-solving skills, better understand the programming language you use, and get to know algorithms you may not be familiar with. If you want to improve your skills in programming, there’s no better way than by writing code. In addition, coding challenges are convenient because they allow you to exercise your skills on a bite-sized problem and rarely require you to build a complete application, so you can usually complete them rather quickly.

Code challenges are also part of most coding interviews. Hiring managers may see the skills listed on your resume, and you may be able to talk like a programmer, but they also want to know that you can write code. By having you solve a coding challenge, they can assess your skills and be sure you can do the job. So working on coding challenges will also help you prepare for job interviews. We’ve collected 20 popular code challenges to get you started.

Learn something new for free

Intro to chatgpt, general programming challenges.

While most code challenges are small in scope, that doesn’t mean they won’t involve a complex solution, so it is best to choose a challenge that stretches your skills but isn’t completely out of your league. Below, we’ve ranked a few coding challenges by their complexity so you can find the best challenge for your skill level.

Basic code challenges

These are good beginner challenges. They may not actually show up in a coding interview, but everyone has to start somewhere. These challenges are good for practicing your skills at using a programming language.

• Build a binary search tree .
• Write a program that prints the numbers from 1 to 100. But for multiples of three, print Fizz instead of the number, and multiples of five, print Buzz. For numbers that are multiples of both three and five, print FizzBuzz .
• Print Hello World in several different ways in a programming language .
• Code in a new language .
• Write a function that will take a given string and reverse the order of the words .
• Write a function that will find the 50th number in the Fibonacci Sequence .
• Write a function that tests if a number, n, is a prime number .

Intermediate code challenges

These code challenges are examples of what might be asked in interviews. There may be a big difference in difficulty compared to the basic challenges. If you get stuck on these, go back to the basics, practice more, and you will get there.

• Write a function to check that a binary search tree is balanced .
• Write a function to reverse the order of words that have punctuation and keep the punctuation in place .
• Given two words (beginWord and endWord) and a dictionary’s word list, find the length of the shortest transformation sequence from beginWord to endWord .
• Write a function that will find the nth number in the Fibonacci Sequence .
• Write a function that will print out all prime numbers in a given string .

Hard code challenges

The point of these challenges is to challenge you, which will help you learn more. These will be similar to the type of work you’ll do on the job. Most of these challenges will be hard but use Big O notation and expect a certain type of performance. If you are struggling with these, search StackOverflow or Google for direction. Many developers have run into these types of problems and will help you find the solution. Just don’t cheat and copy the answer. What good would that do?

• Write a function that inserts a list of n numbers into a binary search tree that runs at O(n log n) time .
• Write a function to reverse the order of words with punctuation and keep the punctuation in place that runs at 0(n) time .
• Write a function that will find the nth number in the Fibonacci Sequence and runs at O(n) time .
• Write a function that tests if a number, n, is a prime number and a function that will print out all prime numbers in a given string as efficiently as possible .

Technology specific challenges

If you want to try some coding challenges that will test your skills on specific technologies, we have a few of those challenges.

Web development code challenges

• Build a web page for your favorite band . A fun challenge can be creating a webpage for your favorite musical artist. Start by using only static HTML , and if you want to challenge yourself, even more, add CSS and JavaScript . Then, for extra credit, build it in a front-end framework like React and make it an interactive experience.
• Recreate a magazine layout using Semantic HTML and CSS Flexbox . It is not always that easy to recreate a design. This code challenge will really test your skills with HTML and CSS by having you recreate a design from scratch on your own. Once you are a working web developer, you will be doing this daily.
• Build a static portfolio site . Once you finish the first two web development challenges listed here, you will have demonstrated your skills in web development. So why not take it a step further and show off those skills to the world or a potential employer by building a portfolio site? With this challenge, you will do just that. You can use HTML and CSS and, if you want, JavaScript. If you need help creating a portfolio, watch the video below for a step-by-step tutorial. And if you want to learn how to use JavaScript to make it interactive, check out Part 2 .

Financial data analysis code challenges

Maximize stock trading profit . This is reportedly a question asked in a Google interview and will test your skills in analyzing financial data . There are three levels to this challenge:

• Basic: Given the daily values of a stock, write a program that will find how you can gain the most with a single buy-sell-trade.
• Intermediate: Given the daily values of a stock over several days n , write a program that will find how you can gain the most with a combination of buy-sell trades.
• Hard: Complete both the basic and intermediate algorithms in the most efficient way possible.

Code challenges are a great way to practice your coding skills or keep yourself from getting rusty. Building complete applications will also teach you a lot, but they can take time to finish. On the other hand, a coding challenge can be completed in an evening and will expose you to new algorithms and programming concepts. They are also part of many coding interviews, so completing a few can help you prepare for a job interview. For more details on the code challenges we have, check out Essential Information on Code Challenges .

Whether you’re looking to break into a new career, build your technical skills, or just code for fun, we’re here to help every step of the way. Check out our blog post about how to choose the best Codecademy plan for you to learn about our structured courses, professional certifications, interview prep resources, career services, and more.

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How to Solve Programming Problems

Written By John Sonmez

Right before the holidays, I said that you had better learn how to solve programming problems .

This time I am going to try and give you some good tools to enable you to get good at solving programming problems.  (Really algorithm type problems specifically.)

Common mistakes

When most programmers are given a programming problem in an interview, they make several key mistakes.  The most severe of those is the improper allocation of time.

If you have heard the saying “measure twice and cut once,” then you are probably familiar with the idea of spending upfront time to make sure something is done right, rather than diving right in.

The most common mistake I see when conducting interviews or watching someone try to solve a programming problem is they try to start writing code as soon as possible.

You must resist this urge.

You really want to make sure you take enough time to understand the problem completely before attempting to solve it.

Another big mistake is trying to over solve the solution on the first iteration.  Keep it simple, don’t try to get fancy.

A simple set of steps

I am going to give you a simple set of steps to follow which you can use for any algorithm type programming problem.

• Read the problem completely twice.
• Solve the problem manually with 3 sets of sample data.
• Optimize the manual steps.
• Write the manual steps as comments or pseudo-code.
• Replace the comments or pseudo-code with real code.
• Optimize the real code.

As much as 70% of our time should be spent in steps 1-3.

Let’s look at each step.

Read the problem completely twice

This is the single most important step.  You may even want to read the problem 3 or 4 times.

You want to make sure you completely understand the problem.  A good test of this is whether or not you can explain the problem to someone else.

I cannot over-emphasize how important this step is!

If you don’t understand the problem, you cannot solve it.  Do not worry about wasting time here, because the better you understand the problem, the easier it will be to solve it.

If you are given any examples along with the problem, make sure you have worked through the examples and understand why the answers are correct for each one.

Solve the problem manually

I am going to tell you perhaps the biggest secret in programming.

“Nothing can be automated that cannot be done manually!”

Programming is automation plain and simple.  You may have the ability to skip the manual steps and jump directly to code, but there is a manual process which is the foundation of any code you write.

It is very important to solve the problem manually first, so that you know what you are going to automate, otherwise you are just slinging code around.  Which while can be fun, will make you look like an idiot in a programming interview and will probably cause you to sweat profusely.

I recommend that you solve the problem with at least three different inputs to make sure you really understand your solution and that it will work for more than one case.

I often use a Mathematical Induction approach if possible.  Using this approach I might try and solve for 1 first, then for 2, then for n.

Also don’t forget to look for corner cases and edge cases and do any examples for those kind of cases you can think of.

It’s very important that when you solve a problem manually, you recognize what your brain is actually doing to solve the problem.  You may need to write out all the things you are normally storing in your head.  You want to be aware of each step, it is easy to gloss over them.

Let’s look at a very basic example, reversing a string.

If I give you a string “Zebra”, and ask you to reverse it, most people will do the following manual steps.

• Write “Zebra” down.
• Start a new word, and put “a” as the first letter.  (Why –> because it is the last letter, we want to start here)
• Put “r” down as the 2nd letter.  (Why –> because it is the next letter backwards from the last letter we copied)
• Put “b” down as the 3rd letter.  (Why –> same as above)

Notice how I write down each little step and why.

Optimize the manual solution

People often don’t realize how valuable this step is.  It is much easier to rearrange and reconstruct and idea or algorithm in your head than it is in code.

It’s well worth the effort to try and optimize the actual solution or simplify it when it is still in the most easily malleable state.

What you want to do here is figure out if there is another way you can solve the problem easier, or if there are some steps you can cut our or simplify.

Let’s look at our string reversal example and see if we can simplify the steps.

We should be able to immediately recognize that we can use a loop here to reduce the manual steps.  Our duplicate why’s for most of our steps tell us that we are doing the same thing over and over for each step, just with different data.

• Start at the last letter in the word and create a new empty word.
• Append the current letter to the new word
• If there is a previous letter, make the previous letter the current letter and start back at 3.

Look how close we are getting to code at this point.  You should be tempted to actually write the code for this.  That is good, it tells you that you have solved and simplified the problem well.  Writing code should now become very easy.

Write pseudo-code or comments

Many times you can skip this step if you have a really good handle on the problem or your previous steps already created a detailed enough description of the solution that coding it is already a 1 to 1 translation.

If you are a beginner or struggle with these kinds of problems, I would go ahead and take the time to do this step anyway though.

What we want to do here is capture all the steps we created and now either put them into our editor as comments or write them as psuedo-code that we can translate to real code.

By doing this, we can know exactly what the structure of the code we are going to write is going to look like which makes the job of filling in the actual code later trivial.

Let’s look at some psudeo-code for reversing a string.

// NewWord = “” // Loop backwards through word to reverse //   NewWord += CurrentLetter // Return NewWord

Pretty simple, but the key thing we have done here is outlined the structure of the code we will write to solve the problem.

Replace comments with real code

This step should be extremely easy at this point.  If you have done all the other steps, this step involves no problem solving at all.

All we do here is take each comment and convert it into a real line of code.

Taking the string reversal, we might end up with something like this.

1 for 1 translation of the comments we created above for real code.

If you struggle here, there are usually two possible reasons:

• You didn’t break down the problem into small enough steps
• You don’t know your programming language well enough to do the conversion

If you didn’t break the problem down enough, try going back to the second step and being as meticulous as possible.  Write out each and every single step.  I know it is a pain, but do it, believe me it will be worth the effort.

If you don’t know your programming language well enough to do the translation, you may need to brush up here on some basic constructs.  Any language you expect to be able to solve algorithm type problems in, you should know how to do the following things:

• Create a list
• Sort a list or array
• Create a map or dictionary
• Loop through a list, or dictionary
• Parse strings
• Convert from string to int, int to string, etc

If you don’t know how to do all of these things.  Stop what you are doing now and learn them. It’s not a very long list, and the benefits will be profound.

Optimize the real code

Sometimes this step isn’t necessary, but it’s worth taking a look at your code and figuring out if you can cut out a few lines or do something simpler.

This is also a good place to make sure all your variables are named with long meaningful names.  I cannot stress enough how important having good names for your variables and methods is for helping the person evaluating your code to understand what you were trying to do.  This is especially important when you make a mistake!

I won’t give an optimization for our trivial example of a string reversal, but a word of advice here is not to get too tricky.  Just try to mainly simplify your code and get rid of duplication.

A few final tips

If you follow this template for solving algorithm type problem, you should do very well in programming interviews, but the key to doing so is having confidence in this process.

The only way you are going to have confidence in this process is to practice it.  It takes a good amount of faith to believe that spending 70% of your 30 minutes to solve a problem just thinking about the problem and not writing any code is the right approach, so make sure you have that faith when you need it.

I’ve talked about using TopCoder to become a better programmer before, and I still recommend it.  Codility.com is another great site I have recently been introduced to.

There is one important step I did not include in the outline above, because I didn’t want to make the process any more complicated than it needed to be.

Many times you will find that a problem itself involves multiple large steps or is very complicated.  In those instances, you will want to try and find a way to cut the problem directly in half and then following the process above for each half.

This method of tackling a problem is called “divide and conquer” and is quite effective.  A good way to know where to break a problem in half is to think about what part of the problem if already given to you would make solving the rest easy.

The programming interview is merely one battle in a larger war: marketing yourself. For the full lowdown, take a look at my course: How to Market Yourself as a Software Developer .

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

[3] Figure 3: Example Algotithm

Verifying your 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.

Figure 4 “ 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!

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

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.

What is a program?

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

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.

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”
• accept number1, accept number2
• 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 Examples

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 [in Python the operator is ==]

> is greater than

< is less than

>= is greater than or equal

<= is less than or equal

<> is not equal to [in Python the operator is !=]

• 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.

= is equal to  [in Python the operator is ==]

<> is not equal to  [in Python the operator is !=]

• 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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11 Beginner Tips for Learning Python Programming

Table of Contents

Tip #1: Code Everyday

Tip #2: write it out, tip #3: go interactive, tip #4: take breaks, tip #5: become a bug bounty hunter, tip #6: surround yourself with others who are learning, tip #7: teach, tip #8: pair program, tip #9: ask “good” questions, tip #10: build something, anything, tip #11: contribute to open source, go forth and learn.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: 11 Beginner Tips for Learning Python

We are so excited that you have decided to embark on the journey of learning Python ! One of the most common questions we receive from our readers is “What’s the best way to learn Python?”

I believe that the first step in learning any programming language is making sure that you understand how to learn. Learning how to learn is arguably the most critical skill involved in computer programming.

Why is knowing how to learn so important? The answer is simple: as languages evolve, libraries are created, and tools are upgraded. Knowing how to learn will be essential to keeping up with these changes and becoming a successful programmer.

In this article, we will offer several learning strategies that will help jump start your journey of becoming a rockstar Python programmer!

Free Download: Get a sample chapter from Python Basics: A Practical Introduction to Python 3 to see how you can go from beginner to intermediate in Python with a complete curriculum, up-to-date for Python 3.8.

Make It Stick

Here are some tips to help you make the new concepts you are learning as a beginner programmer really stick:

Consistency is very important when you are learning a new language. We recommend making a commitment to code every day. It may be hard to believe, but muscle memory plays a large part in programming. Committing to coding everyday will really help develop that muscle memory. Though it may seem daunting at first, consider starting small with 25 minutes everyday and working your way up from there.

Check out the First Steps With Python Guide for information on setup as well as exercises to get you started.

As you progress on your journey as a new programmer, you may wonder if you should be taking notes. Yes, you should! In fact, research suggests that taking notes by hand is most beneficial for long-term retention. This will be especially beneficial for those working towards the goal of becoming a full-time developer, as many interviews will involve writing code on a whiteboard.

Once you start working on small projects and programs, writing by hand can also help you plan your code before you move to the computer. You can save a lot of time if you write out which functions and classes you will need, as well as how they will interact.

Whether you are learning about basic Python data structures (strings, lists, dictionaries, etc.) for the first time, or you are debugging an application, the interactive Python shell will be one of your best learning tools. We use it a lot on this site too!

To use the interactive Python shell (also sometimes called a “Python REPL” ), first make sure Python is installed on your computer. We’ve got a step-by-step tutorial to help you do that. To activate the interactive Python shell, simply open your terminal and run python or python3 depending on your installation. You can find more specific directions here .

Note: For a full guide to the standard Python REPL, check out The Python Standard REPL: Try Out Code and Ideas Quickly .

Now that you know how to start the shell, here are a few examples of how you can use the shell when you are learning:

Learn what operations can be performed on an element by using dir():

The elements returned from dir() are all of the methods (i.e. actions) that you can apply to the element. For example:

Notice that we called the upper() method. Can you see what it does? It makes all of the letters in the string uppercase! Learn more about these built-in methods under “Manipulating strings” in this tutorial .

Learn the type of an element:

Use the built-in help system to get full documentation:

Import libraries and play with them:

Run shell commands:

When you are learning, it is important to step away and absorb the concepts. The Pomodoro Technique is widely used and can help: you work for 25 minutes, take a short break, and then repeat the process. Taking breaks is critical to having an effective study session, particularly when you are taking in a lot of new information.

Breaks are especially important when you are debugging. If you hit a bug and can’t quite figure out what is going wrong, take a break. Step away from your computer, go for a walk, or chat with a friend.

In programming, your code must follow the rules of a language and logic exactly, so even missing a quotation mark will break everything. Fresh eyes make a big difference.

Speaking of hitting a bug, it is inevitable once you start writing complex programs that you will run into bugs in your code. It happens to all of us! Don’t let bugs frustrate you. Instead, embrace these moments with pride and think of yourself as a bug bounty hunter.

When debugging, it is important to have a methodological approach to help you find where things are breaking down. Going through your code in the order in which it is executed and making sure each part works is a great way to do this.

Once you have an idea of where things might be breaking down, insert the following line of code into your script import pdb; pdb.set_trace() and run it. This is the Python debugger and will drop you into interactive mode. The debugger can also be run from the command line with python -m pdb <my_file.py> .

Make It Collaborative

Once things start to stick, expedite your learning through collaboration. Here are some strategies to help you get the most out of working with others.

Though coding may seem like a solitary activity, it actually works best when you work together. It is extremely important when you are learning to code in Python that you surround yourself with other people who are learning as well. This will allow you to share the tips and tricks you learn along the way.

Don’t worry if you don’t know anyone. There are plenty of ways to meet others who are passionate about learning Python! Find local events or Meetups or join PythonistaCafe , a peer-to-peer learning community for Python enthusiasts like you!

It is said that the best way to learn something is to teach it. This is true when you are learning Python. There are many ways to do this: whiteboarding with other Python lovers, writing blog posts explaining newly learned concepts, recording videos in which you explain something you learned, or simply talking to yourself at your computer. Each of these strategies will solidify your understanding as well as expose any gaps in your understanding.

Pair programming is a technique that involves two developers working at one workstation to complete a task. The two developers switch between being the “driver” and the “navigator.” The “driver” writes the code, while the “navigator” helps guide the problem solving and reviews the code as it is written. Switch frequently to get the benefit of both sides.

Pair programming has many benefits: it gives you a chance to not only have someone review your code, but also see how someone else might be thinking about a problem. Being exposed to multiple ideas and ways of thinking will help you in problem solving when you got back to coding on your own.

People always say there is no such thing as a bad question, but when it comes to programming, it is possible to ask a question badly. When you are asking for help from someone who has little or no context on the problem you are trying to solve, its best to ask GOOD questions by following this acronym:

• G : Give context on what you are trying to do, clearly describing the problem.
• O : Outline the things you have already tried to fix the issue.
• O : Offer your best guess as to what the problem might be. This helps the person who is helping you to not only know what you are thinking, but also know that you have done some thinking on your own.
• D : Demo what is happening. Include the code, a traceback error message, and an explanation of the steps you executed that resulted in the error. This way, the person helping does not have to try to recreate the issue.

Good questions can save a lot of time. Skipping any of these steps can result in back-and-forth conversations that can cause conflict. As a beginner, you want to make sure you ask good questions so that you practice communicating your thought process, and so that people who help you will be happy to continue helping you.

Make Something

Most, if not all, Python developers you speak to will tell you that in order to learn Python, you must learn by doing. Doing exercises can only take you so far: you learn the most by building.

For beginners, there are many small exercises that will really help you become confident with Python, as well as develop the muscle memory that we spoke about above. Once you have a solid grasp on basic data structures (strings, lists, dictionaries, sets), object-oriented programming , and writing classes, it’s time to start building!

What you build is not as important as how you build it. The journey of building is truly what will teach you the most. You can only learn so much from reading Real Python articles and courses. Most of your learning will come from using Python to build something. The problems you will solve will teach you a lot.

There are many lists out there with ideas for beginner Python projects. Here are some ideas to get you started:

• Number guessing game
• Simple calculator app
• Dice roll simulator
• Bitcoin Price Notification Service

If you find it difficult to come up with Python practice projects to work on, watch this video . It lays out a strategy you can use to generate thousands of project ideas whenever you feel stuck.

In the open-source model, software source code is available publicly, and anyone can collaborate. There are many Python libraries that are open-source projects and take contributions. Additionally, many companies publish open-source projects. This means you can work with code written and produced by the engineers working in these companies.

Contributing to an open-source Python project is a great way to create extremely valuable learning experiences. Let’s say you decide to submit a bugfix request: you submit a “pull request” for your fix to be patched into the code.

Next, the project managers will review your work, providing comments and suggestions. This will enable you to learn best practices for Python programming, as well as practice communicating with other developers.

For additional tips and tactics that will help you break into the open-source world, check out the video embedded below:

Now that you have these strategies for learning, you are ready to begin your Python journey! Find Real Python’s Beginners Roadmap for Learning here ! We also offer a beginner’s level Python course , which uses interesting examples to help you learn programming and web development.

Happy Coding!

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Follow these steps to solve any Dynamic Programming interview problem

By Nikola Otasevic

Despite having significant experience building software products, many engineers feel jittery at the thought of going through a coding interview that focuses on algorithms. I’ve interviewed hundreds of engineers at Refdash , Google, and at startups I’ve been a part of, and some of the most common questions that make engineers uneasy are the ones that involve Dynamic Programming (DP).

Many tech companies like to ask DP questions in their interviews. While we can debate whether they’re effective in evaluating someone’s ability to perform in an engineering role, DP continues to be an area that trips engineers up on their way to finding a job that they love.

Dynamic Programming — Predictable and Preparable

One of the reasons why I personally believe that DP questions might not be the best way to test engineering ability is that they’re predictable and easy to pattern match. They allow us to filter much more for preparedness as opposed to engineering ability.

These questions typically seem pretty complex on the outside, and might give you an impression that a person who solves them is very good at algorithms. Similarly, people who may not be able to get over some mind-twisting concepts of DP might seem pretty weak in their knowledge of algorithms.

The reality is different, and the biggest factor in their performance is preparedness. So let’s make sure everyone is prepared for it. Once and for all.

7 Steps to solve a Dynamic Programming problem

In the rest of this post, I will go over a recipe that you can follow to figure out if a problem is a “DP problem”, as well as to figure out a solution to such a problem. Specifically, I will go through the following steps:

• How to recognize a DP problem
• Identify problem variables
• Clearly express the recurrence relation
• Identify the base cases
• Decide if you want to implement it iteratively or recursively
• Add memoization
• Determine time complexity

Sample DP Problem

For the purpose of having an example for abstractions that I am going to make, let me introduce a sample problem. In each of the sections, I will refer to the problem, but you could also read the sections independently of the problem.

Problem statement:

In this problem, we’re on a crazy jumping ball, trying to stop, while avoiding spikes along the way.

Here are the rules:

1) You’re given a flat runway with a bunch of spikes in it. The runway is represented by a boolean array which indicates if a particular (discrete) spot is clear of spikes. It is True for clear and False for not clear.

Example array representation:

2) You’re given a starting speed S. S is a non-negative integer at any given point, and it indicates how much you will move forward with the next jump.

3) Every time you land on a spot, you can adjust your speed by up to 1 unit before the next jump.

4) You want to safely stop anywhere along the runway (does not need to be at the end of the array). You stop when your speed becomes 0. However, if you land on a spike at any point, your crazy bouncing ball bursts and it’s game over.

The output of your function should be a boolean indicating whether we can safely stop anywhere along the runway.

Step 1: How to recognize a Dynamic Programming problem

First, let’s make it clear that DP is essentially just an optimization technique. DP is a method for solving problems by breaking them down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. The next time the same subproblem occurs, instead of recomputing its solution, you simply look up the previously computed solution. This saves computation time at the expense of a (hopefully) modest expenditure in storage space.

Recognizing that a problem can be solved using DP is the first and often the most difficult step in solving it. What you want to ask yourself is whether your problem solution can be expressed as a function of solutions to similar smaller problems.

In the case of our example problem, given a point on the runway, a speed, and the runway ahead, we could determine the spots where we could potentially jump next. Furthermore, it seems that whether we can stop from the current point with the current speed depends only on whether we could stop from the point we choose to go to next.

That is a great thing, because by moving forward, we shorten the runway ahead and make our problem smaller. We should be able to repeat this process all the way until we get to a point where it is obvious whether we can stop.

Recognizing a Dynamic Programming problem is often the most difficult step in solving it. Can the problem solution be expressed as a function of solutions to similar smaller problems?

Step 2: Identify problem variables

Now we have established that there is some recursive structure between our subproblems. Next, we need to express the problem in terms of the function parameters and see which of those parameters are changing.

Typically in interviews, you will have one or two changing parameters, but technically this could be any number. A classic example of a one-changing-parameter problem is “determine an n-th Fibonacci number”. Such an example for a two-changing-parameters problem is “Compute edit distance between strings”. If you’re not familiar with these problems, don’t worry about it.

A way to determine the number of changing parameters is to list examples of several subproblems and compare the parameters. Counting the number of changing parameters is valuable to determine the number of subproblems we have to solve. It’s also important in its own right in helping us strengthen the understanding of the recurrence relation from step 1.

In our example, the two parameters that could change for every subproblem are:

• Array position (P)

One could say that the runway ahead is changing as well, but that would be redundant considering that the entire non-changing runway and the position (P) carry that information already.

Now, with these 2 changing parameters and other static parameters, we have the complete description of our sub-problems.

Identify the changing parameters and determine the number of subproblems.

Step 3: Clearly express the recurrence relation

This is an important step that many rush through in order to get into coding. Expressing the recurrence relation as clearly as possible will strengthen your problem understanding and make everything else significantly easier.

Once you figure out that the recurrence relation exists and you specify the problems in terms of parameters, this should come as a natural step. How do problems relate to each other? In other words, let’s assume that you have computed the subproblems. How would you compute the main problem?

Here is how we think about it in our sample problem:

Because you can adjust your speed by up to 1 before jumping to the next position, there are only 3 possible speeds, and therefore 3 spots in which we could be next.

More formally, if our speed is S, position P, we could go from (S, P) to:

• (S, P + S) ; # if we do not change the speed
• (S — 1, P + S — 1) ; # if we change the speed by -1
• (S + 1, P + S + 1) ; # if we change the speed by +1

If we can find a way to stop in any of the subproblems above, then we can also stop from (S, P). This is because we can transition from (S, P) to any of the above three options.

This is typically a fine level of understanding of the problem (plain English explanation), but you sometimes might want to express the relation mathematically as well. Let’s call a function that we’re trying to compute canStop. Then:

canStop(S, P) = canStop(S, P + S) || canStop(S — 1, P + S — 1) || canStop(S + 1, P + S + 1)

Woohoo, it seems like we have our recurrence relation!

Recurrence relation: Assuming you have computed the subproblems, how would you compute the main problem?

Step 4: Identify the base cases

A base case is a subproblem that doesn’t depend on any other subproblem. In order to find such subproblems, you typically want to try a few examples, see how your problem simplifies into smaller subproblems, and identify at what point it cannot be simplified further.

The reason a problem cannot be simplified further is that one of the parameters would become a value that is not possible given the constraints of the problem.

In our example problem, we have two changing parameters, S and P. Let’s think about what possible values of S and P might not be legal:

• P should be within the bounds of the given runway
• P cannot be such that runway[P] is false because that would mean that we’re standing on a spike
• S cannot be negative, and a S==0 indicates that we’re done

Sometimes it can be a little challenging to convert assertions that we make about parameters into programmable base cases. This is because, in addition to listing the assertions if you want to make your code look concise and not check for unnecessary conditions, you also need to think about which of these conditions are even possible.

In our example:

• P < 0 || P >= length of runway seems like the right thing to do. An alternative could be to consider m aking P == end of runway a base case. However, it is possible that a problem splits into a subproblem which goes beyond the end of the runway, so we really need to check for inequality.
• This seems pretty obvious. We can simply check if runway[P] is false .
• Similar to #1, we could simply check for S < 0 and S == 0. However, here we can reason that it is impossible for S to be < 0 because S decreases by at most 1, so it would have to go through S == 0 case beforehand. Ther efore S == 0 is a sufficient base case for the S parameter.

Step 5: Decide if you want to implement it iteratively or recursively

The way we talked about the steps so far might lead you to think that we should implement the problem recursively. However, everything that we’ve talked about so far is completely agnostic to whether you decide to implement the problem recursively or iteratively. In both approaches, you would have to determine the recurrence relation and the base cases.

To decide whether to go iteratively or recursively, you want to carefully think about the trade-offs .

Stack overflow issues are typically a deal breaker and a reason why you would not want to have recursion in a (backend) production system. However, for the purposes of the interview, as long as you mention the trade-offs, you should typically be fine with either of the implementations. You should feel comfortable implementing both.

In our particular problem, I implemented both versions. Here is python code for that: A recursive solution: (original code snippets can be found here )

An iterative solution: (original code snippets can be found here )

Step 6: Add memoization

Memoization is a technique that is closely associated with DP. It is used for storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Why are we adding memoization to our recursion? We encounter the same subproblems which, without memoization, are computed repeatedly. Those repetitions very often lead to exponential time complexities.

In recursive solutions, adding memoization should feel straightforward. Let’s see why. Remember that memoization is just a cache of the function results. There are times when you want to deviate from this definition in order to squeeze out some minor optimizations, but treating memoization as a function result cache is the most intuitive way to implement it.

This means that you should:

• Store your function result into your memory before every return statement
• Look up the memory for the function result before you start doing any other computation

Here is the code from above with added memoization (added lines are highlighted): (original code snippets can be found here )

In order to illustrate the effectiveness of memoization and different approaches, let’s do some quick tests. I will stress test all three methods that we have seen so far. Here is the set up:

• I created a runway of length 1000 with spikes in random places (I chose to have a probability of a spike being in any given spot to be 20%)
• initSpeed = 30
• I ran all functions 10 times and measured the average time of execution

Here are the results (in seconds):

You can see that the pure recursive approach takes about 500x more time than the iterative approach and about 1300x more time than the recursive approach with memoization. Note that this discrepancy would grow rapidly with the length of the runway. I encourage you to try running it yourself.

Step 7: Determine Time complexity

There are some simple rules that can make computing time complexity of a dynamic programming problem much easier. Here are two steps that you need to do:

• Count the number of states — this will depend on the number of changing parameters in your problem
• Think about the work done per each state. In other words, if everything else but one state has been computed, how much work do you have to do to compute that last state?

In our example problem, the number of states is |P| * |S|, where

• P is the set of all positions (|P| indicates the number of elements in P)
• S is the set of all speeds

The work done per each state is O(1) in this problem because, given all other states, we simply have to look at 3 subproblems to determine the resulting state.

As we noted in the code before, |S| is limited by length of the runway (|P|), so we could say that the number of states is |P|² and because work done per each state is O(1), then the total time complexity is O(|P|²).

However, it seems that |S| can be further limited, because if it were really |P|, it is very clear that stopping would not be possible because you would have to jump the length of the entire runway on the first move.

So let’s see how we can put a tighter bound on |S|. Let’s call maximum speed S. Assume that we’re starting from position 0. How quickly could we stop if we were trying to stop as soon as possible and if we ignore potential spikes?

In the first iteration, we would have to come at least to the point (S-1), by adjusting our speed at zero by -1. From there we would at a minimum go by (S-2) steps forward, and so on.

For a runway of length L , the following has to hold:

=> (S-1) + (S-2) + (S-3) + ….+ 1 < L

=> S*(S-1) / 2 < L

=> S² — S — 2L < 0

If you find roots of the above function, they will be:

r1 = 1/2 + sqrt(1/4 + 2L) and r2 = 1/2 — sqrt(1/4 + 2L)

We can write our inequality as:

(S — r1) * (S — r2) < ; 0

Considering that S — r2 > 0 for any S > 0 and L > 0, we need the following:

S — 1/2 — sqrt(1/4 + 2L) < ; 0

=> S < 1/2 + sqrt(1/4 + 2L)

That is the maximum speed that we could possibly have on a runway of a length L. If we had a speed higher than that, we could not stop even theoretically, irrespective of the position of the spikes.

That means that the total time complexity depends only on the length of the runway L in the following form:

O(L * sqrt(L)) which is better than O(L²)

O(L * sqrt(L)) is the upper bound on the time complexity

Awesome, you made it through! :)

The 7 steps that we went through should give you a framework for systematically solving any dynamic programming problem. I highly recommend practicing this approach on a few more problems to perfect your approach.

Here are some next steps that you can take

• Extend the sample problem by trying to find a path to a stopping point. We solved a problem that tells you whether you can stop, but what if you wanted to also know the steps to take in order to stop eventually along the runway? How would you modify the existing implementation to do that?
• If you want to solidify your understanding of memoization, and understand that it is just a function result cache, you should read about decorators in Python or similar concepts in other languages. Think about how they would allow you to implement memoization in general for any function that you want to memoize.
• Work on more DP problems by following the steps we went through. You can always find a bunch of them online (ex. LeetCode or GeeksForGeeks ). As you practice, keep in mind one thing: learn ideas, don’t learn problems. The number of ideas is significantly smaller and it’s an easier space to conquer which will also serve you much better.

When you feel like you’ve conquered these ideas, check out Refdash where you are interviewed by a senior engineer and get a detailed feedback on your coding, algorithms, and system design.

Originally published at Refdash blog . Refdash is an interviewing platform that helps engineers interview anonymously with experienced engineers from top companies such as Google, Facebook, or Palantir and get a detailed feedback. Refdash also helps engineers discover amazing job opportunities based on their skills and interests.

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Do You Solve Programming Problems or Complete Exercises?

The Difference Matters for Building True Expertise

"An exercise is a question you immediately know how to answer. A problem is a question you do not know how to answer initially." – Professor Paul Zeitz

When discussing programming, the terms "problem" and "exercise" tend to be used interchangeably. However, experts like mathematician Paul Zeitz draw an important distinction between the two. Exercises provide targeted practice, while problems teach deeper skills.

Cultivating the ability to tackle ambiguous, unfamiliar problems is what separates the good from the great developers. Luckily, you can train this capacity like any other skill.

In this guide, we‘ll cover:

• Key differences between programming problems vs. exercises
• The benefits problems provide beyond coding ability
• Tactics to apply when taking on problems
• How to shift your mindset to embrace uncertain problems
• Studies on skill development rates from problem practice

Understanding the vital yet distinct roles problems and exercises play will help you structure your learning for maximum growth.

Programming Exercises vs Problems

First, let‘s clearly define our terms with examples across coding languages:

Exercises provide repetition and practice for recently learned concepts, syntax, methods, etc. Given explicit instructions and predictable steps, you code an unambiguous solution.

• Sort an array of objects alphabetically on a key
• Find the minimum value in an array of numbers
• Print the keys from a dictionary object
• Convert a string to title case
• Overload a method to handle different data types
• Implement an interface in a class

Problems , on the other hand, contain uncertainty on how to arrive at a solution. With no prescribed path, you independently figure out approach through research, analysis, and creative thinking.

• Given unlabeled location data, categorize points as shops, restaurants, etc using ML
• Detect clicks outside an element to close a dropdown menu
• Find the most common word(s) in text documents
• Compress image files without significant quality loss
• Create a chess game allowing two networked players

Note the clear contrast. Exercises strengthen existing knowledge, while problems build new problem-solving skills. Mastering both accelerates expertise.

Why Problems Matter More

Solving coding problems yields a multitude of metacognitive benefits beyond faster syntax or API mastery from exercises. Let‘s analyze further.

1. Problems Develop Critical Thinking

Deconstructing ambiguous problems requires gathering information, making logical connections, questioning assumptions – all facets of critical thinking.

In a survey by the Business Higher Education Forum, critical thinking ranked as the most valued skill for new hires across industries like tech, finance, healthcare and more. Why?

"Critical thinkers are self-directed, self-disciplined, self-monitored and self-corrective in their thinking." – Linda Elder, President of Foundation for Critical Thinking

This ability to strategically analyze unfamiliar situations and devise solutions permeates every role today, not just programming. Troubleshooting production bugs, evaluating new technologies, communicating technical concepts to non-technical stakeholders all leverage critical thinking.

You cannot teach this skill directly. It emerges as a byproduct of wrestling with knotty, complex problems.

2. Problems Build Tenacity

Have you heard the phrase "the struggle bus"? That feeling when you‘re staring blankly at a screen with no idea what functions or frameworks hold the key?

Problems take you on a ride on the struggle bus, testing your patience and persistence. But emerging out the other side breeds confidence to persevere through future unknowns. Learning scientists call this grit .

In a study by University of Michigan, grittier programmers outperformed their peers in hackathons – building more creative solutions in the 36 hour contests. They attributed this to grit developed through prior struggles with programming problems.

This tenacity serves beyond coding challenges. Startup market fits, production fires, technology shifts – the tech workplace is in constant flux. Being no stranger to struggle is invaluable.

3. Problems Spark Creativity

Exercises have clear boundaries. But problems are open-ended, causing you to contemplate possibilities beyond surface solutions.

Say you‘re building an e-commerce site. An exercise may be "allow sorting products by price." Straightforward to implement by calling .sort() .

But a problem may be "recommend products to match user interests." Myriad unpredictable solutions exist here utilizing recommendation systems, natural language processing, predictive analytics, etc.

With no defined path, you imagine innovative approaches, consulting diverse resources. This problem-driven innovation is what built algorithms powering Netflix, Spotify, Uber.

Bottom line – exercises reinforce skills, but problems reveal breakthroughs.

Tactics for Taking On Problems

Approaching programming problems methodically is key. Let‘s outline actionable tactics top developers use:

Adopt a Growth Mindset

Stanford psychologist Carol Dweck coined the term "growth mindset" – the belief abilities are developed, not innate. Those with a growth mindset embrace challenges, view failures as learning, and thus accomplish more over time.

Compare this to a "fixed mindset" – avoiding challenges, getting defensive about mistakes, and plateauing early on.

Those with fixed mindsets experience programming problems as threats. Those with growth mindsets see exciting opportunities. Adjust your inner monologue to frame problems as a chance to build mastery, not demonstrate deficiencies.

Break Down the Ambiguity

Problems naturally contain uncertainty. Don‘t let this paralyze you! Sketch out what you know, admit what you don‘t, and systematically fill knowledge gaps.

Here are thought processes experts use to dismantle ambiguity:

Reframe the problem – Rephrase or visualize issues from different lenses

Research – Consult documentation and Google til pieces take shape

List assumptions – Determine what you‘re taking for granted

Define inputs & outputs – What goes in and out clarifies scope

Outline milestones – Break into gradual steps if monumental

Attack vagueness right up front before coding. This discovery process itself will unlock realizations.

Watch for Patterns

While every programming problem is unique, common patterns emerge:

• Constraints – Hardware limitations, privacy policies, regulatory rules
• Edge cases – Blank inputs, overflow numbers, strings as input rather than ints
• Overfitting – When solving very narrow specialized cases rather than general ones
• Resource usage – Runtime speed, computational complexity, memory usage

Identifying patterns helps detect hidden issues upfront. Make considerations for each pattern part of your problem analysis routine.

Try Simple Cases First

Resist the instinct to prematurely optimize for complex use cases. Instead, break the principle of "making the simple case work first."

Let‘s say I‘m writing an algorithm to parse nutritional facts on food labels. I‘d start by handling one food item first, ignoring plural values for now.

Once my code extracts calories, carbs, etc from one label, I‘d iterate on handling multiple items. This cycle of simplifying cases prevents getting lost while also building confidence through small wins.

Studies on Problem Solving & Skill Development

The distinction between programming problems vs exercises may seem semantic. But research in learning science reveals tangible impacts on skill development.

For example, a multi-year study by the University of New Mexico tracked 300+ students across computer science courses. They measured time spent solving ambiguous problems vs narrow coding exercises.

Here‘s how the most skillful students spent their time:

Those in the top 10th percentile for programming competence dedicated over 50% of their coding time to wresting with knotty problems. Compare that to weaker performers who worked almost exclusively on exercises.

This aligns with a concept called deliberate practice introduced by Dr. Anders Ericsson . Deliberate practice is focused time spent stretching skills just beyond current capability. Problems provide this stretch – exercises generally do not.

Based on skill level studies, experts recommend coding time be split:

• 75% problems – Majority time spent tackling ambiguous open-ended problems
• 25% exercises – Minority time reinforcing fundamentals with exercises

Of course, adjusting ratios based on experience level is expected. But research clearly shows problems accelerate competence, while exercises maintain it.

Adopt Both Exercises & Problems for Optimal Growth

We‘ve covered quite a bit of ground contrasting programming problems and exercises. To recap:

Exercises provide targeted practice with predefined steps – great for cementing new knowledge.

Problems require uncovering solutions to ambiguous situations – builds deeper critical thinking and creativity.

Benefits – Problem solving develops critical thinking, grit, innovation, and overall readiness for dynamic tech roles.

Both are indispensable. Exercises establish foundations, while problems build higher cognitive aptitudes. Master programmers artfully balance both, emphasizing problem practice especially.

The next time you embark on honing your coding chops, consider whether you‘re working on exercises or solving problems. Be intentional about scheduling time for both. But make sure problems claim the lion‘s share on your calendar.

Consistency is key. Like all metaskills, creativity, critical thinking, and mental stamina improve gradually by regular stimulation. Make time for problem practice every single day without fail.

Soon you‘ll notice once-impenetrable problems now seem like routine exercises. And when stumped again by the next challenge, smile knowing your skills are ascending swiftly to greater heights!

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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7 Problem-Solving Skills That Can Help You Be a More Successful Manager

Discover what problem-solving is and why it's important for managers. Explore the steps of the process and learn about seven problem-solving skills.

Managers regularly use problem-solving skills to oversee the day-to-day operations of a particular department and sometimes a whole company. Managers with good problem-solving skills can help ensure companies run smoothly and prosper.

If you're a current manager or are striving to become one, read this guide to discover what problem-solving skills are and why it's important for managers to have them. Learn the steps of the problem-solving process, and explore seven skills that can help make problem-solving easier and more effective.

What is problem-solving?

Problem-solving is both an ability and a process. As an ability, problem-solving can help resolve issues in different environments, such as home, school, abroad, and social situations. As a process, problem-solving involves a series of steps for finding solutions to questions or concerns that arise throughout life.

The importance of problem-solving for managers

Managers deal with problems regularly, whether supervising a staff of two or 100. Workplaces can benefit in several ways when people solve problems quickly and effectively. These include:

Greater creativity

Higher productivity

Increased job fulfillment

Satisfied clients or customers

Better cooperation and cohesion

Improved environments for employees and customers

7 skills that make problem-solving easier

Companies depend on managers who can solve problems adeptly. Although problem-solving is a skill in its own right, a subset of seven skills can help make it easier. These include analysis, communication, emotional intelligence, resilience, creativity, adaptability, and teamwork.

1. Analysis

As a manager, you'll solve each problem by assessing the situation first. Then, you’ll use analytical skills to distinguish between ineffective and effective solutions.

2. Communication

Effective communication plays a significant role in problem-solving, particularly when others are involved. Some skills that can help enhance communication at work include active listening, speaking with an even tone and volume, and supporting verbal information with written communication.

3. Emotional intelligence

Emotional intelligence is the ability to recognise and manage emotions in any situation. People with emotional intelligence usually solve problems calmly and systematically, which often yields better results.

4. Resilience

Emotional intelligence and resilience are closely related traits. Resiliency is the ability to cope quickly and bounce back from difficult situations. Those who possess resilience can often accurately interpret people and situations, which can be incredibly advantageous when difficulties arise.

5. Creativity 

When brainstorming solutions to problems, creativity can help you to think outside the box. Problem-solving strategies can be enhanced with the application of creative techniques. You can use creativity to:

Approach problems from different angles

Improve your problem-solving process

Spark creativity in your employees and peers

6. Adaptability

Adaptability is the capacity to adjust to change. When a particular solution doesn't work, an adaptable person can revisit the concern to think up another one without getting frustrated.

7. Teamwork

Finding a solution to a problem regularly involves working in a team. Good teamwork requires being comfortable working with others and collaborating with them, which can result in better problem-solving overall.

Steps of the problem-solving process

Effective problem-solving involves five essential steps. One way to remember them is through the IDEAL model, created in 1984 by psychology professors John D. Bransford and Barry S. Stein. The steps to solving problems in this model include identifying a problem, defining the goals you hope to achieve, exploring potential solutions, choosing and acting on it, and looking at (or evaluating) the outcome [ 1 ].

1. Identify the problem and root out its cause.

To solve a problem, you must first admit that one exists to find its root cause. Finding the cause of the problem may involve asking questions like:

Can the problem be solved?

How big of a problem is it?

Why do I think the problem is occurring?

What are some things I know about the situation?

What are some things I don't know about the situation?

Are there any people who contributed to the problem?

Are there materials or processes that contributed to the problem?

Are there any patterns I can identify?

2. Define the goals you hope to achieve.

Every problem is different. The goals you hope to achieve when problem-solving depends on the scope of the problem. Some examples of goals you might set include:

Gather as much factual information as possible.

Brainstorm many different strategies to come up with the best one.

Be flexible when considering other viewpoints.

Articulate clearly and encourage questions so everyone involved is on the same page.

Be open to other strategies if the chosen strategy doesn't work.

Stay positive throughout the process.

3. Explore potential solutions.

Once you've defined the goals you hope to achieve when problem-solving, it's time to start the process. This involves steps that often include fact-finding, brainstorming, prioritising solutions, and assessing the cost of top solutions in terms of time, labour, and money.

4. Choose a solution and act on it.

Evaluate the pros and cons of each potential solution and choose the one most likely to solve the problem within your given budget, abilities, and resources. Once you choose a solution, it's important to commit to it and see it through. Draw up a plan of action for implementation and share it clearly and effectively, both verbally and in writing, with all involved parties. Make sure everyone understands their role for a successful conclusion.

5. Look at (or evaluate) the outcome.

Evaluation offers insights into your current situation and future problem-solving. When evaluating the outcome, ask yourself questions like:

Did the solution work?

Will this solution work for other problems?

What changes would you have made?

Would another solution have worked better?

Improve your problem-solving skills.

Problem-solving is an important skill for managers, and it involves analysing the situation, communicating effectively, and coming up with creative solutions. As a current or future manager looking to build your problem-solving skills, it is often helpful to take a professional course. Consider Improving Communication Skills offered by the University of Pennsylvania on Coursera. You'll learn how to boost your ability to persuade, ask questions, negotiate, apologise, and more. 

You might also consider taking Emotional Intelligence: Cultivating Immensely Human Interactions , offered by the University of Michigan on Coursera. You'll explore the interpersonal and intrapersonal skills common to people with emotional intelligence and learn how emotional intelligence is connected to team success and leadership.

Article sources

Indian Institute of Public Administration. “ Problem-Solving , https://www.iipa.org.in/cms/public/training_course/59.” Accessed 20 August 2024. 

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Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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In this video, we will understand the Majority Element problem! 🧠 Perfect for coding enthusiasts, students gearing up for interviews, or professionals sharpening their problem-solving skills, this video offers a comprehensive guide to understanding and solving one of the most essential challenges in array processing.

What You'll Learn:

• Defining the Majority Element: We start by explaining what constitutes a majority element in an array and why it’s crucial in the realm of algorithms.
• Examples with Detailed Explanations: Follow along as we explore various examples to identify the majority element in different arrays. Understand how certain elements qualify and why some arrays lack a majority element altogether.
• Naive Approach: Learn the basic, yet fundamental, method of solving the Majority Element problem using a nested loop approach. We’ll break down the logic, show you how to implement it, and discuss its time complexity (O(n²)) and space complexity (O(1)).
• Efficient Approach (Moore’s Voting Algorithm): Step up your game with this optimized method that reduces the time complexity to O(n). We’ll guide you through the two-step process of finding the majority candidate and validating it, ensuring you grasp the algorithm's power and efficiency.

Key Points Covered:

• Introduction to the Majority Element problem.
• An in-depth explanation of the Naive Approach.
• Detailed walkthrough of Moore’s Voting Algorithm.
• Comparison of time and space complexity for both methods.
• Practical coding examples to solidify your understanding.
• Enhance your problem-solving toolkit with essential algorithms.
• Prepare for coding interviews with clear and concise explanations.
• Access practical examples in various programming languages to practice and perfect your skills.

Welcome to "Around the DawgHouse" where we update you multiple times each week on the latest news and information from the different sports. Around The DawgHouse is also a daily feature on DawgNation Daily.

ATHENS — Jared Wilson’s Achilles injury caused a problem for Georgia during fall camp.

The fourth-year junior is expected to take over for Sedrick Van Pran-Ganger as Georgia’s starting center. While Wilson was dealing with an Achilles injury, the Bulldogs moved All-American guard Tate Ratledge to center.

“Tate’s gotten a lot of work there through our OTAs, training camp days, walk-throughs,” Georgia coach Kirby Smart said. “Tate has had a lot of reps, and he’s been right next to the center making all the calls. The mental part of it’s been great, and his snaps have been really good too. He’s just creating value for himself in that he can play multiple positions now.”

Ratledge was named a First-Team AP All-American at guard this week . Dylan Fairchild was named a Second Team All-American this week. And Georgia very much loves what Micah Morris brings to the table as well.

Wilson is back healthy and will be Georgia’s starting center this season . Even with Georgia’s three-guard situation, that will continue to be the case.

“Jared’s (Wilson) been back and practiced in scrimmage Saturday and done some good things,” Smart said. “We’ve been controlling his volume, but he’s been out there working and getting some continuity. He has the largest body of work over the last two to three years at center of anybody.”

So Georgia will have to find a way to rotate three guards who could start for any program in the country and keep all of them happy. Calling it a problem might be a stretch, but it’s one Georgia will gladly deal with as opposed to dealing with a deficit.

Fairchild started nine games for Georgia last season. Ratledge started 13 last season while Morris is still waiting to make his first career start.

The Bulldogs have employed a rotation at tackle before, famously doing so when it had Amairus Mims, Broderick Jones and Warren McLendon all on the team.

This trio of guards is arguably more talented than that trio of tackles.

It would be easy for the intense level of competition to create a negative atmosphere in the offensive line room. But it’s actually the opposite, where there’s a very healthy respect and admiration between everyone.

“It’s a good thing to have people like Dylan Fairchild on the line,” offensive tackle Earnest Greene said of Fairchild. “That’s why you come here to Georgia. to play with and compete with good people at your position. It’s definitely a reason you come here, having people like Dylan on the line.”

Fairchild was equally complimentary of Morris and Ratledge. He cited Ratledge as a huge influence on his own game while noting the brotherly bond he shares with Morris.

“I know if I’ve got a bad day he’s got my back and I appreciate that all the time,” Fairchild said. “If he’s got a bad day I’ve got his back, and I’d just say all of us on the line, we kind of take each other’s back. If one of us is lacking in an area we’ll call it out and raise the standard. I think that he does a great job, and everybody on the o-line does a great job of holding each other accountable and just being best friends.”

At some point, Georgia will have to figure out which combination of guards and tackles help compose Georgia’s best five-man lineup. There’s a strong belief that Wilson is the best center among the group, even with the positive progress Ratledge has made.

Xavier Truss, Monroe Freeling and Greene will be factors at tackle. At guard, it’s like to come down to Ratledge, Fairchild and Morris. With three men fighting for two spots.

Georgia wasn’t able to figure or use it’s best five-man combination last season against Alabama, in part because Mims left the game in the first quarter with an injury. Truss moved to right tackle and Georgia tried both Morris and Fairchild at left guard, with the former getting the first shot at playing.

The Bulldogs will have a long season to figure out who makes up that combination. And while injuries may certainly be a factor, as they already have as Wilson has shown, whoever makes up that group will have undoubtedly earned it.

“I think we’re confident in whoever we’re going to put out there from week one all the way through wherever we end up in the playoffs,” Fairchild said. “I think we’ll be fine no matter what we do.”

Dylan Fairchild shares his thoughts on the Georgia offensive line

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Ensure High-Quality Data Powers Your AI

• Thomas C. Redman

The utility and impact of this technology depends on the data that goes into it.

AI does not need to fail on a global scale to cause enormous damage — to individuals, companies, and societies. Models frequently get things wrong, hallucinate, drift, and can collapse. Good AI comes from good data, but data quality is an enormous organization-wide issue (and opportunity), yet most companies have neglected it. Companies need to understand the nuances of the problem they’re trying to solve, get the data right (both by having the right data for that problem and by ensuring that the data is error-free), assign responsibility for data quality in the short term, and then push quality efforts upstream in the longer-term.

Twenty years ago, mortgage-backed securities and collateralized debt obligations were all the rage. These new financial products were, initially, a wonder: they helped put millions of people into homes and make billions for banks. Then things went horribly wrong, and they nearly tanked the global economy.

• Thomas C. Redman , “the Data Doc,” is President of Data Quality Solutions . He helps companies and people  chart their courses to data-driven futures with special emphasis on quality, analytics, and organizational capabilities. His latest book, People and Data: Uniting to Transform Your Organization (Kogan Page) was published Summer 2023.

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Home / Programs / Healthcare / MSN / Master of Science in Nursing – Nurse Educator / Developing Critical-thinking and Problem-solving Skills in Nursing Education

Developing Critical-thinking and Problem-solving Skills in Nursing Education

• Published On: August 20, 2024

Nursing education is a cornerstone of healthcare, preparing the next generation of nurses to manage complex clinical scenarios competently and confidently. Critical-thinking and problem-solving skills are essential for nurses to navigate the uncertainties of patient care effectively.

To meet the need for educators adept at critical thinking and problem solving, programs like Northwest Missouri State University’s online Master of Science in Nursing (MSN) – Nurse Educator program offer valuable opportunities. Northwest’s program equips nurse educators with the tools necessary to cultivate these skills in their students.

In particular, program courses such as Instructional Strategies in Learning and Teaching and Curriculum Development are pivotal in shaping nurse educators . Such coursework provides a framework for effective teaching methodologies that not only convey technical nursing skills but also foster critical-thinking and problem-solving abilities.

Understanding Critical Thinking in Nursing

Nurses must have strong critical-thinking skills in order to practice with proficiency and precision. It primarily involves making well-informed and judicious clinical decisions that directly impact patient care. Critical thinking in this context involves several key elements, including the following:

• Logical questioning . Nurses must be adept at posing relevant and insightful questions about patient conditions, treatments and outcomes. This skill helps uncover necessary information that might not be immediately apparent, facilitating more accurate diagnosis and care planning.
• Effective data analysis . The ability to analyze data from patient records, lab results and other diagnostic tools is crucial. Nurses must interpret this data correctly to make informed decisions about patient care. This analysis forms the backbone of clinical reasoning and allows nurses to anticipate potential complications.
• Critical evaluation . Nurses need to critically evaluate the outcomes of their interventions. Evaluation includes assessing the effectiveness of treatments and understanding patient responses to adjust care plans accordingly. It’s a dynamic process that requires ongoing attention and adjustment based on real-time feedback.

Synchronous vs. Asynchronous Learning

The debate between synchronous and asynchronous education models in nursing has significant implications for developing critical-thinking and decision-making skills. Synchronous learning, with real-time interaction between instructors and students, often fosters a more immediate and dynamic exchange of ideas, which can enhance problem-solving skills. On the other hand, asynchronous learning allows for flexibility and self-paced study. This form of learning can support deeper reflection and self-assessment.

In fact, students in Northwest’s online MSN – Nurse Educator program and complete the coursework in as few as 12 months. The flexible format allows students to complete their work from anywhere, and students collaborate with faculty mentors and nurse experts to maximize progress and learning.

Bridging the Gap Between Education and Employer Expectations

It’s important to recognize potential gaps between nursing graduates’ skills and employer expectations. Team-based learning can address this discrepancy, promoting collaborative learning and helping students apply theoretical knowledge in practical settings.

Advanced education programs, like Northwest’s, help students develop better communication skills, learn to delegate and prioritize tasks, and make more informed clinical decisions. All of these foundational elements help bridge that gap between education and real-world expectations.

Impact of Cognitive Skills Training

Incorporating cognitive skills training within nursing curricula is critical for preparing nurses to meet the demands of contemporary healthcare settings. Research underscores the necessity of integrating cognitive training to equip nurses with a robust skill set essential for clinical excellence. Consider the following benefits of strengthening cognitive skills:

• Memory retention . Nursing practice requires the ability to recall vast amounts of medical information accurately and quickly. As a result, techniques that enhance memory retention are vital. These might include mnemonic devices, repetition exercises and simulation-based learning experiences.
• Enhanced concentration . The ability to maintain concentration amid the bustling and often chaotic environment of healthcare facilities is crucial. Training that improves focus can include mindfulness exercises, time management strategies, and cognitive exercises designed to boost mental resilience and the ability to stay focused on tasks for extended periods.
• Improved problem-solving skills . Problem solving is at the heart of nursing, requiring nurses to swiftly assess situations, recognize potential complications and devise effective solutions. Training that hones these skills often involves case studies, critical-incident analyses and scenario-based simulations that challenge nurses to think critically and react under pressure.

The Future of Modern Healthcare Practice

Developing critical-thinking and problem-solving skills in nursing education is not just about teaching the fundamentals of nursing. It also involves preparing nurses to think on their feet in varied and challenging situations.

As healthcare continues evolving, educational programs must evolve as well, ensuring they remain relevant and effective. The online MSN – Nurse Educator program at Northwest is an example of how institutions are responding to these needs, preparing nurse leaders to handle and impart the complexities of modern healthcare practice.

Learn more about Northwest’s online Master of Science in Nursing – Nurse Educator program .

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self-preservation without replication —

Research ai model unexpectedly modified its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..

Benj Edwards - Aug 14, 2024 8:13 pm UTC

On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.

Further Reading

"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."

Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.

• A screenshot of example code the AI Scientist wrote to extend its runtime, provided by Sakana AI. Sakana AI

While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.

Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:

Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.

Endless scientific slop

Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.

"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."

According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.

Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.

"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."

Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."

And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."

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