What Is Problem Solving? How Software Engineers Approach Complex Challenges

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

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

What Is Problem Solving?

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

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

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

The Importance of Problem-Solving Skills for Software Engineers

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

Driving Development Forward

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

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

Innovation and Optimization

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

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

Increasing Efficiency and Productivity

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

Improving Software Quality

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

Problem-Solving Techniques in Software Engineering

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

Decomposition

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

Abstraction

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

Algorithmic Thinking

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

Parallel Thinking

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

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

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

Testing and Validation

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

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Evaluating Problem-Solving Skills

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

Recognizing Problem-Solving Skills in Candidates

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

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

Assessing Problem-Solving Skills

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

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

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

Key Takeaways

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

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

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

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

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How To Approach A Coding Problem ?

Solving a DSA (Data Structures and Algorithms) Problem is quite tough. In This article, we help you not only solve the problem but actually understand it, It’s not about just solving a problem it’s about understanding the problem. we will help to solve DSA problems on websites like Leetcode, CodeChef, Codeforces, and Geeksforgeeks. the importance of solving a problem is not just limited to job interviews or solve problems on online platform, its about develop a problem solving abilities which is make your prefrontal cortex strong, sharp and prepared it to solve complex problem in future, not only DSA problems also in life.

These steps you need to follow while solving a problem:

– Understand the question, read it 2-3 times. – Take an estimate of the required complexity. – find, edge cases based on the constraints. – find a brute-force solution. ensure it will pass. – Optimize code, ensure, and repeat this step. – Dry-run your solution(pen& paper) on the test cases and edge cases. – Code it and test it with the test cases and edge cases. – Submit solution. Debug it and fix it, if the solution does not work.

How-to-Approach-a-Coding-Problem

Understand The Question

firstly read it 2-3 times, It doesn’t matter if you have seen the question in the past or not, read the question several times and understand it completely. Now, think about the question and analyze it carefully. Sometimes we read a few lines and assume the rest of the things on our own but a slight change in your question can change a lot of things in your code so be careful about that. Now take a paper and write down everything. What is given (input) and what you need to find out (output)? While going through the problem you need to ask a few questions yourself…

  • Did you understand the problem fully?
  • Would you be able to explain this question to someone else?
  • What and how many inputs are required?
  • What would be the output for those inputs
  • Do you need to separate out some modules or parts from the problem?
  • Do you have enough information to solve that question? If not then read the question again or clear it to the interviewer.

                Estimate of the required complexity

Look at the constraints and time limit. This should give you a rough idea of the expected time and space complexity. Use this step to reject the solutions that will not pass the limits. With some practice, you will be able to get an estimate within seconds of glancing at the constraints and limits.

              Find, edge cases 

In most problems, you would be provided with sample input and output with which you can test your solution. These tests would most likely not contain the edge cases. Edge cases are the boundary cases that might need additional handling. Before jumping on to any solution, write down the edge cases that your solution should work on. When you try to understand the problem take some sample inputs and try to analyze the output. Taking some sample inputs will help you to understand the problem in a better way. You will also get clarity that how many cases your code can handle and what all can be the possible output or output range.

Constraints 

0 <= T <= 100

1 <= N <= 1000

-1000 <= value of element <= 1000

Find a brute-force Solution

A brute-force solution for a DSA (Data Structure and Algorithm) problem involves exhaustively checking all possible solutions until the correct one is found. This method is typically very time-consuming and not efficient, but can be useful for small-scale problems or as a way to verify the correctness of a more optimized solution. One example of a problem that could be solved using a brute-force approach is finding the shortest path in a graph. The algorithm would check every possible path until the shortest one is found.

Break Down The Problem

When you see a coding question that is complex or big, instead of being afraid and getting confused that how to solve that question, break down the problem into smaller chunks and then try to solve each part of the problem. Below are some steps you should follow in order to solve the complex coding questions… 

  • Make a flow chart or a UML for the problem at hand.
  • Divide the problem into sub-problems or smaller chunks.
  • Solve the subproblems. Make independent functions for each subproblem.
  • Connect the solutions of each subproblem by calling them in the required order, or as necessary.
  • Wherever it’s required use classes and objects while handling questions (for real-world problems like management systems, etc.)

Optimize your Code

Always try to improve your code. Look back, analyze it once again and try to find a better or alternate solution. We have mentioned earlier that you should always try to write the right amount of good code so always look for the alternate solution which is more efficient than the previous one. Writing the correct solution to your problem is not the final thing you should do. Explore the problem completely with all possible solutions and then write down the most efficient or optimized solution for your code. So once you are done with writing the solution for your code below are some questions you should ask yourself. 

Optimizing a solution in DSA (Data Structure and Algorithm) refers to improving the efficiency of an algorithm by reducing the time and/or space complexity. This can be done by using techniques such as dynamic programming, greedy algorithms, divide and conquer, backtracking, or using more efficient data structures.

It’s important to note that the optimization process is not always straightforward and it can be highly dependent on the specific problem and constraints. The optimization process usually starts with a brute force approach, and then various techniques will be applied to make the algorithm more efficient. The optimization process will often require a trade-off between time and space complexity.

Also, measuring and analyzing the performance of the algorithm is an essential step in the optimization process. The use of mathematical notation and analysis tools like Big O notation and complexity analysis, can help to understand the performance of an algorithm and decide which one is the best.

  • Does this code run for every possible input including the edge cases.
  • Is there an alternate solution for the same problem?
  • Is the code efficient? Can it be more efficient or can the performance be improved?
  • How else can you make the code more readable?
  • Are there any more extra steps or functions you can take out?
  • Is there any repetition in your code? Take it out.

Below is the alternate solution for the same problem of the array which returns even numbers… 

          Dry-run your solution

Dry-running a solution on test cases and edge cases involves manually going through the steps of the algorithm with sample inputs and verifying that the output is correct. This process can help to identify any bugs or errors in the code, as well as ensure that the algorithm is correctly handling all possible inputs, including edge cases.

When dry-running your solution, it’s important to consider both the expected test cases and any unexpected edge cases that may arise. Edge cases are inputs that are at the boundaries of the problem’s constraints, for example, the maximum or minimum values.

To dry-run the solution, you will need to:

  • Write down the sample test case inputs and expected outputs.
  • Go through the steps of the algorithm manually, using the test case inputs.
  • Compare the output of the algorithm to the expected output, to ensure the solution is correct.
  • Repeat the process for each test case and edge case.
  • Dry-running your solution on test cases and edge cases can help you to identify any issues with your algorithm, and make any necessary adjustments before running the code on a computer.

             Code & Test it On Edge Cases

After dry-running your solution and verifying that it is right, the next step is to code it and test it using the test cases and edge cases.

To code the solution, you will need to:

  •  write the code for the algorithm.
  • Make sure to include any necessary data structures and methods.
  • Test the code with sample inputs, including the test cases and edge cases that were used during the dry-run.
  • When testing the code, it’s important to not only check for the expected outputs, but also for any unexpected behavior or errors. Testing with edge cases is especially important as it can reveal bugs or errors that might not be present in other test cases.

It’s also a good practice to test the code with additional test cases and edge cases, to further ensure the correctness and robustness of the solution.

Once the code has been tested and all the bugs have been fixed, you can submit it.

 Submit solution

After coding and testing the solution on the sample test cases, the next step is to submit it, usually to a platform for review or for a contest.

The submission process can depending on the platform, but basically it involves submitting the code and any necessary documentation. After the submission, the solution is usually reviewed by other participants or judges, and feedback is provided on whether the solution is correct or if there are any errors. If the solution is wrong or does not work as expected, the next step is to debug and fix it. Debugging is the process of identifying and resolving errors in the code. This can involve using tools such as a debugger, print statements, or logging to find the source of the problem.

Once the error has been identified, the next step is to fix it. This can involve making changes to the code, data structures, or algorithms used. Once the changes have been made, it’s important to test the solution again to ensure that the error has been resolved and that the solution is correct.

If the solution is correct, you can submit it again or move on to other problems.

It’s important to note that the debugging and fixing process can be an iterative one and it may take several iterations to get the solution working correctly.

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Problem Solving

Foundations course, introduction.

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

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

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

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

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

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

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

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

Lesson overview

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

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

Understand the problem

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

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

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

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

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

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

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

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

Divide and conquer

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

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

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

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

Solving Fizz Buzz

To demonstrate this workflow in action, let’s solve a common programming exercise: Fizz Buzz, explained in this wiki article .

Understanding the problem

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

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

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

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

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

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

Writing the pseudocode

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

Dividing and conquering

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Knowledge check

This section contains questions for you to check your understanding of this lesson on your own. If you’re having trouble answering a question, click it and review the material it links to.

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

Additional resources

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

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

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What is Pseudocode? How to Use Pseudocode to Solve Coding Problems

You might be wondering what pseudocode is and why it's so useful for writing computer programs.

But before we jump into pseudocode, let's refresh our memories about what programming and coding are, in the simplest sense.

Programming is the manifestation of logic. A program is a set of instructions that defines the behaviour of your software application. Writing code is how you implement it for the machine.

What is Pseudocode?

Pseudocode literally means ‘fake code’. It is an informal and contrived way of writing programs in which you represent the sequence of actions and instructions (aka algorithms) in a form that humans can easily understand.

You see, computers and human beings are quite different, and therein lies the problem.

The language of a computer is very rigid: you are not allowed to make any mistakes or deviate from the rules. Even with the invention of high-level, human-readable languages like JavaScript and Python, it’s still pretty hard for an average human developer to reason and program in those coding languages.

With pseudocode, however, it’s the exact opposite. You make the rules. It doesn’t matter what language you use to write your pseudocode. All that matters is comprehension.

In pseudocode, you don't have to think about semi-colons, curly braces, the syntax for arrow functions, how to define promises, DOM methods and other core language principles. You just have to be able to explain what you're thinking and doing.

Benefits of Writing Pseudocode

When you're writing code in a programming language, you’ll have to battle with strict syntax and rigid coding patterns. But you write pseudocode in a language or form with which you're very familiar.  

Since pseudocode is an informal method of program design, you don’t have to obey any set-out rules. You make the rules yourself.

Pseudocode acts as the bridge between your brain and computer’s code executor. It allows you to plan instructions which follow a logical pattern, without including all of the technical details.

Pseudocode is a great way of getting started with software programming as a beginner. You won’t have to overwhelm your brain with coding syntax.

In fact, many companies organize programming tests for their interviewees in pseudocode. This is because the importance of problem solving supersedes the ability to ‘hack’ computer code.

You can get quality code from many platforms online, but you have to learn problem solving and practice it a lot.

Planning computer algorithms with pseudocode makes you meticulous. It helps you explain exactly what each line in a software program should do. This is possible because you are in full control of everything, which is one of the great features of pseudocode.

Example of Pseudocode

Pseudocode is a very intuitive way to develop software programs. To illustrate this, I am going to refer back to a very simple program I wrote in my last article :

When a user fills in a form and clicks the submit button, execute a ValidateEmail function. What should the function do?

  • Derive an email regular expression (regex) to test the user's email address against.
  • Access the user's email from the DOM and store it in a variable. Find and use the right DOM method for that task.
  • With the email value now accessed and stored, create a conditional statement:
  • If the email format doesn’t match the rule specified by the regex, access the element with the myAlert id attribute and pass in the “Invalid Email” message for the user to see.
  • Else, if the above condition isn’t true and the email address format actually matches with the regex, check to see if the database already has such an email address. If it already does, access the element with the myAlert id attribute and pass in the “Email exists!” message for the user to see.
  • Now, if both of these conditions aren’t met, (that is the email format matches the regex and the database doesn’t have such an email address stored yet), push the users email address into the database and pass in the “Successful!” message for the user to see.

Once you are done outlining the various steps you want your code to take, everything becomes easier and clearer. Now, let’s turn that psedocode into real JavaScript code:

All you have to do at this stage is find the programming language constructs that will help you achieve each of your steps. Noticed how seamless the transition from pseudocode to actual code became? That’s how effective writing pseudocode can be for program design.

Pseudocode is also a great way to solve programming-related problems when you're struggling with them. For those practising programming in coding challenge platforms like CodeWars , pseudocode can be of immense help.

How to Solve Programming Problems with Pseudocode

Solving programming problems can be hard. Not only do you have the logical part to reckon with, but also the technical (code forming) part as well. I recently uncovered a brilliant and effective formula for solving tricky coding problems.

Here are the steps you can follow to solving programming problems with pseudocode:

Step 1: Understand what the function does

First, you need to understand that all a function does is (optionally) accept data as input, work on the data little by little, and finally return an output. The body of the function is what actually solves the problem and it does so line by line.

Step 2: Make sure you understand the question

Next, you need to read and understand the question properly. This is arguably the most important step in the process.

If you fail to properly understand the question, you won’t be able to work through the problem and figure out the possible steps to take. Once you identify the main problem to be solved you'll be ready to tackle it.

Step 3: Break the problem down.

Now you need to break down the problem into smaller parts and sub-problems. With each smaller problem you solve, you'll get closer to solving the main problem.

It helps to represent these problem solving steps in the clearest and most easily understandable way you can – which is psedocode!

  • Start solving: open and use tools like Google, Stack Overflow, MDN, and of course freeCodeCamp! :)
  • For every step of the problem that you solve, test the output to make sure you’re on the right path. Keep solving these small problems until you arrive at the final solution.

I picked up this highly effective formula from Aaron Jack and I think you’ll benefit from it. Check out his video about how to solve coding problems:

As you can see, pseudocode is a very useful strategy for planning computer programs.

Of course, you have to remember that pseudocode is not a true representation of a computer program. While using pseudocode to plan your algorithm is great, you will ultimately have to translate it into an actual computer-readable program. This means that you'll eventually need to learn how to program in a real programming language.

Taking up coding challenges online is a great way to learn how to program because, as they say, practice makes perfect. But when you try your next challenge, don’t forget to implement pseudocode in the process!

You can check out some of my other programming-related posts on my personal blog . I am also available on Twitter .

Thank you for reading and see you soon.

P/S: If you are learning JavaScript, I created an eBook which teaches 50 topics in JavaScript with hand-drawn digital notes. Check it out here .

Helping you learn how to code one tutorial at a time. Online creator and entrepreneur.

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How to Solve Coding Challenges: A Comprehensive Guide

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How to Solve Coding Challenges: A Comprehensive Guide

A step-by-step approach to effectively solve coding challenges, prepare for coding interviews, and land dev jobs..

Nhut Nguyen's photo

Table of contents

1. understand the problem, 2. plan your approach, 3. pseudocode, 4. write code, 5. test your code, 6. optimize and refactor, 7. documentation, 8. review and proofread, 9. submit or share, 10. reflect and learn, 11. practice regularly.

In the programming world, coding challenges are a common way to evaluate a programmer's problem-solving abilities and coding skills. Whether you're preparing for a technical interview, honing your coding skills, or participating in programming competitions, the ability to tackle coding challenges is a valuable asset.

This comprehensive guide will walk you through a step-by-step approach to solving coding challenges. It helped me prepare for the coding interviews and land several jobs in my dev career.

The first and most crucial step in solving any coding challenge is thoroughly understanding the problem statement. To do this:

Read Carefully : Begin by reading the problem statement carefully. Pay attention to all the details, requirements, and constraints.

Sample Inputs and Outputs : Review the sample inputs and outputs provided in the problem statement. This will help you understand the expected behavior of your solution.

Clarify Ambiguities : If you encounter any ambiguities or uncertainties in the problem statement, don't hesitate to seek clarification. Contact the challenge provider or your instructor to ensure you clearly understand the problem.

Before you start writing code, it's essential to plan your approach. Consider the following strategies:

Break It Down : If the problem seems complex, break it down into smaller, more manageable subproblems. Solving each subproblem individually can make the overall task more achievable.

Data Structures and Algorithms : Think about the data structures and algorithms you might use. Choose the most suitable ones based on the problem's requirements. This step is crucial for optimizing your solution.

Edge Cases : Identify potential edge cases and corner cases for the problem. Consider how your code should handle these situations. It's often a good idea to address these cases later in your solution.

I always write down my ideas before typing code.

Before diving into coding, creating a pseudocode or an outline of the steps you plan to take is helpful. Pseudocode helps you structure your thoughts and provides a roadmap for your coding journey. While writing pseudocode:

Use plain language to describe the steps concisely.

Avoid detailed syntax; focus on the algorithm's logic.

Make sure your pseudocode captures the essential elements of your solution.

With a clear plan and pseudocode, it's time to start writing the actual code. Follow these guidelines:

Use a Familiar Language : Stick to a programming language you're comfortable with. You'll be more efficient and effective when working with a language you know well.

Follow Your Plan : Translate your pseudocode or plan into actual code. Try to maintain a one-to-one correspondence between your plan and your code. This can help you stay organized and reduce the chances of making mistakes.

Clean Code : Write clean and well-organized code. Use meaningful variable names and include comments to explain your code's purpose and any complex logic.

Testing is a critical part of the coding challenge process. Thoroughly test your code to ensure it behaves correctly under various conditions. Here's how to approach testing:

Test with Sample Inputs : Test your code with the sample inputs and outputs in the problem statement. This helps you verify that your code meets the basic requirements.

Edge Cases : Test your code with edge cases, such as empty inputs, minimum and maximum possible values, or any other unusual situations. This ensures your code handles exceptional scenarios gracefully.

Debugging : If you encounter errors or unexpected behavior during testing, use tools and techniques to identify and resolve issues. Pay attention to error messages and use print statements or debugging tools to trace the code's execution.

Once your code passes the initial tests, consider optimizing it for efficiency. Optimization involves improving the code's runtime performance and memory usage. Here are some optimization techniques:

Algorithmic Optimization : Revisit the algorithms and data structures you chose. Can more efficient alternatives achieve the same results with fewer resources?

Time and Space Complexity : Analyze your code's time and space complexity. Aim to reduce these complexities by optimizing loops, reducing redundant computations, and minimizing memory usage.

Benchmarking : Compare the performance of your code with various inputs. Benchmarking can help you identify bottlenecks and areas for improvement.

Remember that optimization should come after your code is correct. Don't sacrifice correctness for premature optimization.

Documentation is often overlooked but plays a crucial role in code quality and readability. When documenting your code:

Add comments to explain the purpose of functions, classes, and complex code segments.

Include comments for any non-trivial logic or algorithmic steps.

Write clear and concise docstrings for functions, describing their inputs, outputs, and usage.

Adequate documentation makes it easier for others to understand and maintain your code. It's also helpful for future reference when you revisit your code.

Before your coding challenge is complete, take some time to review and proofread your code. Here's what to look for:

Errors and Bugs : Carefully review your code for syntax errors, logical issues, or runtime exceptions.

Compliance with Requirements : Double-check that your solution meets all the requirements in the problem description.

Code Style : Ensure your code adheres to a consistent code style, such as using proper indentation, following naming conventions and using consistent formatting.

The final step in solving a coding challenge depends on the context:

Technical Interviews : If you're solving a coding challenge as part of a technical interview, be prepared to discuss your code and approach with the interviewer. Provide clear explanations of your thought process.

Programming Competitions : You should submit your code as instructed in competitive programming or coding competitions. Make sure to follow the submission guidelines carefully.

Personal Projects or Learning : If you're solving coding challenges for personal development or learning, consider sharing your solution with others through coding platforms or personal portfolios. Share instructions on how to run and test your code.

After completing a coding challenge, take some time to reflect on your solution:

Consider how you approached the problem. Were there any aha moments or insights during the process?

Think about how you could have solved the problem differently or more efficiently.

Identify areas where you encountered challenges or your knowledge is lacking, and use this as an opportunity to learn and grow.

The more you practice solving coding challenges, the more proficient you become. Regular practice is essential for honing your problem-solving skills and coding abilities. Consider these tips for effective practice:

Variety of Challenges : Work on various coding challenges, including those related to data structures, algorithms, and specific domains (e.g., dynamic programming, graph theory, string manipulation).

Online Platforms : Participate in online coding platforms like LeetCode , HackerRank , CodeSignal , or Project Euler . These platforms offer a wide range of challenges and a supportive community.

Keep a Portfolio : To showcase your solutions and progress, consider maintaining a coding challenge portfolio. This can be beneficial for job applications and personal development.

Competitions : If you're interested in competitive programming, participate in coding competitions like ACM ICPC or TopCoder's Single Round Matches .

I chose Leetcode for its extensive collection of diverse and well-crafted problems.

Remember that solving coding challenges is a skill that improves with practice. Don't be discouraged by complex challenges; each is an opportunity to learn and grow.

Solving coding challenges is an essential skill for programmers, whether you're preparing for a technical interview, honing your coding abilities, or participating in programming competitions. Following a structured approach, understanding the problem, planning your solution, writing clean code, testing rigorously, optimizing when necessary, and practicing regularly can enhance your problem-solving skills and help you become a more proficient coder.

Coding challenges provide a platform for continuous learning and self-improvement. Through regular practice and thoughtful reflection on your solutions, you can tackle increasingly complex challenges and gain confidence in your coding abilities.

So, embrace coding challenges as a valuable opportunity to learn, grow, and demonstrate your programming skills. Happy coding!

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Problem-Solving Skills for Software Developers: Why & How to Improve

how to improve problem-solving skills for software developers

Problem-solving skills go hand-in-hand with software development. Learn some great problem-solving techniques and tips for improvement here!

Software developer jobs today require that you possess excellent problem-solving skills , and for good reason. Unfortunately, there seems to be a sort of talent gap when it comes to this one skill required of all software developers.

Troubleshooting and problem resolution are both informally and formally taught, but you mostly find that software developers have to learn problem-solving skills on their own. This is true for self-taught developers , obviously, but also even for those with software engineering degrees or who’ve graduated from coding boot camps.

This is why it’s necessary to acquaint yourself with the problem-solving process, whether you are a newbie or an experienced developer. In this article, we’ll explore everything you need to know about problem-solving so you can 10x your software development career.

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What are Problem-Solving Skills?

As a developer, what do we mean by problem-solving? Let’s attempt a simple definition.

In software development, problem-solving is the process of using theories and research to find solutions to a problem domain, while testing different ideas and applying best practices to achieve a desired result. Problem-solving also has to do with utilizing creativity and logical thought processes to identify problems and resolve them with software.

Becoming a great software developer hinges more on learning algorithms than programming languages or frameworks . And algorithms are simply step-by-step instructions to solve a given problem.

Read More : How to Build a Software Engineer Portfolio (With Examples & Tips)

Why are impeccable problem-solving skills crucial?

Making good use of a computer language can be likened to being a skilled writer. An effective writer must know how to construct sentences and use grammar appropriately. There’s more to writing than just knowing all the words in the dictionary, and that’s how it works for developers, too.

You have different tasks to work on as a software developer, including perhaps designing, coding, and troubleshooting. Much of your time will be spent on identifying problems, spotting and correcting bugs, and making sense of codebases from before you started working there. Being ingenious at problem-solving is essential in creating incredible solutions to issues that arise throughout software development.

To demonstrate ingenuity, let’s consider Google’s autocomplete tool as an example.

The autocomplete tool is built to suggest related terms in the search bar as you type. The idea behind the tool is to reduce more than 200 years of time spent typing daily and to help users save time by up to 25% while typing.

Here’s what had to be done:

  • To activate real-time completion of suggestions, the UI experience and JavaScript had to be implemented.
  • Next, since users could type just about anything, the autocomplete suggestions had to be compiled into a sensible list dependent on user input.
  • Then, Google had to create a back-end sustainability system for this function. Doing this meant massively increasing its infrastructure to accommodate all forms of data query and HTTP requests.
  • Finally, the user interface had to be refined by software engineers in order to make sure that every user enjoyed a worthwhile experience. So they employed Google Trends to power the auto-completion tool while using algorithms to take out explicit or offensive predictions in line with Google’s auto-completion policy.

This is just one of Google’s innumerable problem-solving examples, but it’s clear to see that solving problems involves more than just telling a computer to do stuff. It’s about your ability to come up with parameters rightly tailored to target users so they can meet their goals.

So why must developers focus on problem-solving at work?

Software developers work with a wide range of people and departments, and it’s common to discover that some clients and teams find it difficult to define what they want. As a problem solver, it’s up to you to help them identify their needs and communicate their thoughts in an effective way.

Of course, you’ll need time and practice to develop your problem resolution ability. That’s because it’s less about solving problems faster but more about coming up with the best solution . And then you’ll need to deploy that solution.

Read More : Common Interview Questions for Software Developer Jobs (Non-Technical)

Types of problem-solving skills

Now let’s talk about four types of problem-solving skills for developers:

1.  Parallel thinking

As a software developer, parallel thinking is a crucial skill necessary to perform optimally. This makes it possible for you to carry out two tasks that complement each other at the same time (like an optimized form of multitasking skills). Being able to reorder tasks to boost parallel execution can help to improve your output and save valuable time .

2. Dissecting broad and/or complex goals

When it comes to building software, you will need to effectively outline the steps and tasks necessary to achieve your goal. Developers must learn to break large and complex tasks into smaller assignments because this is an important skill that will help you create results with precision.

3. Reimplementing existing solutions

You don’t always need to reinvent the wheel. Part of being an effective software developer comes with being able to use already existing tools before even thinking of creating new solutions. Developing problem-solving skills is very much connected to finding solutions that already exist and reusing them.

4. Abstraction

Keep in mind that goals tend to evolve. So if your client comes up with new ideas, that will mean changing your design goals and reordering your tasks. A good programmer must learn to create solutions in such a way that does not require a complete redesign from scratch.

You also have to become adept at abstracting problems so that your solutions can get them resolved so long as they aren’t entirely different from the original issue. You don’t necessarily have to abstract every aspect to avoid more complications being created. This calls for balance by abstracting only where necessary without making narrow decisions.

Read More : Learn 8 Great Benefits of Working From Home

4 Important Tips & Strategies for Improving Problem-Solving Skills

To keep your problem-solving skills and techniques from growing weaker over time, you need to exercise them non-stop. As they say: practice makes perfect!

To train the problem-solving side of your brain, these four tips and strategies can help you improve your abilities:

1. Make problem-solving a part of your life

Never restrict yourself to working on problems only during work hours. Don’t make it a chore, but, instead, do things that make problem-solving look fun. The game of chess, solving puzzles, and playing video games that compel you to think critically will help strengthen your problem-solving skills, and you can tell your significant other you are advancing your career! 🙂

When you come to a complex problem in your life, whether it’s budgeting for a home or renovating the downstairs bathroom, approach it both creatively and critically. Ask yourself: What would a great software engineer do in this situation?

2. Use different platforms to solve problems

Proffer solutions to a set of problems without restricting yourself to one platform. Using different platforms and tools regularly helps make sure you become flexible as a problem-solver. And it makes sense, because there really is no universal solution for the different problems that pop up in your line of work. Trying out different platforms to solve different problems helps you to keep an open mind and enables you to test out different techniques when looking to find solutions.

Read More : 12 Common Mistakes Keeping You From Landing Your First Developer Job

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3. Be open to assistance from external sources

Part of being a good software developer comes with being able to ask for help and also accept all forms of feedback. You might need a different opinion or a new set of eyes to help find the most fitting solution to some problems. It makes sense to view building problem-solving skills as more of a team effort rather than a personal journey.

Have an open mind and heart to function not only as an individual but also as a collective. It’s a utopian working environment where everyone supports each other to become better versions of themselves. So if you come across an issue that keeps you stuck, get help! You may find someone who has a more refined framework or method you never knew existed or would have thought of using. You could then learn from them and add their solution to your toolkit.

Get feedback often, as well. This could be the catalyst to making improvements to your processes and evolving them into something truly refined.

4. Tackle new problems using lessons from past solutions

As you practice and finesse your ability to identify problems and find solutions, you’ll begin to notice patterns. It’s more like developing your toolbox armed with a wide range of solutions that have proved useful in the past. So when problems emerge, you will notice how easy it is to take some of those old solutions and apply them to the new problem.

The more you attempt to apply creativity in solving problems, the more you grow your skills. In the long run, that will help you find the right solutions faster and apply them to a wide range of problems more naturally. It’s all about improving the effectiveness and efficiency with which you tackle new problems while applying only the best possible solutions.

Read More : How to Stay Motivated at Work

3 Complementary Skills to Improve to Become a Good Problem Solver

Developing software is mostly about problem-solving at the very core before even writing your first lines of code. You have to identify problems that can be solved using software. Then you have to go on to understand how people try to solve such problems in real life.

It’s up to you to come up with a framework that allows you to take both the problem and the solution and convert them into computer code. And you have to do this in such a way that makes the software even more efficient and effective than a human.

While going through this process, developers also have to handle other problems such as deadline deliveries, checking for bugs and fixing them, and collaborate across teams. So, supporting skills must not be overlooked.

Software developers must build interpersonal skills and collaboration skills . Being able to empathize, accept feedback, handle criticism, listen intently, and show respect for others are all important characteristics and abilities necessary for teamwork, and, thus, necessary for solving problems on the job.

Read More : 5 Ways to Stand Out & Get Noticed in Your Current Development Job

Communication

No one is an island, and that’s true when you consider how software engineers work. Building software requires keeping up with clients and teammates and other departments. You can’t afford to be a Lone Ranger, at least not 100% of the time, and that’s why employers always look for good communication skills.

Being a good software developer also involves how well you can break down very complex concepts to laypeople. You want to be the kind of person who fixes a problem and is able to explain how you were able to do it. It’s all about your ability to be clear and articulate about every aspect of your work. And you want to be able to communicate not just verbally but also in written form.

To build your communication skills as a developer, you can learn from more experienced people and observe how they interact with their clients. And, don’t forget, with more and more companies becoming global enterprises and going remote, it’s important to brush up on your intercultural communication skills , as well.

Logical thinking

The difference between elite software developers and average ones is often said to be logical thinking. The ability to process thoughts logically is important, because you’ll often spend most of your time finding and fixing bugs rather than writing code.

Problems can show up from just about anywhere, even from what seems to be the most insignificant errors. So, your ability to detect software issues and solve these problems using deductive thought processes is a vital ingredient to your success as a software developer.

Read More : Questions to Ask at Interviews for Software Engineering Jobs

Problem-Solving Stages & Practices

There are countless problem-solving processes and various schools of thought regarding the best way to approach problems whenever they arise. To solve that problem, we’ve pooled some of these frameworks together to come up with a comprehensive approach to problem-solving.

Step 1 – Define the problem

You have to first start with problem identification. Knowing what you are dealing with is important, because you don’t want to risk spending valuable time applying wrong solutions. Avoid making automatic assumptions. Even when the symptoms look familiar, you want to investigate properly because such signs could be pointing to something else entirely.

Problems in software development come in different sizes and scopes. You could be having trouble getting some aspects of the product to respond in the desired way. Or maybe you’re having issues trying to decipher a codebase section where you can no longer communicate with the original developers. Sometimes, the problem could come in the form of an unfamiliar error message and you’re at loss.

Once you’re able to define the problem, make sure to document it.

Step 2 – Analyze the problem

Now it’s time to carry out problem analysis . Before deciding what problem resolution methods to adopt, it’s necessary to find out all there is to the issue, which builds on our first step. This will make it easier to come up with ideas and solutions later on.

Problem analysis isn’t always a walk in the park. There are times when the problem involves a very small mistake such as failing to import a package correctly or a small syntax error. Other times, however, it could be such a huge error, like the entire program acting differently than what you want. There might be no alarms or blinking red lights to tell you what the exact problem is.

If you encounter such situations, you can find answers by articulating the problem. Document what you intend to do, what you’ve done, the original intention for the program, and where you currently are. Communication comes in handy here, of course, not just in your documentation, but also in how you relay it to your teammates.

Read More : Got a Busy Developer Schedule? Here’s How to Keep Learning & Make Time

Step 3 – Brainstorm

This step has to do with generating ideas, and you can benefit from discussing the problem with a team and then coming up with ways to get it fixed. Keep in mind that problem-solving at work involves interacting with a diverse group of people where the individuals have unique skill sets and experiences.

Many developers tend to neglect the previous steps and rush straight into brainstorming. That’s definitely not a good way to go about problem-solving. The idea is not to skip the important steps in the process.

Once you get to the point where ideas need to be generated, do not discard any, because this step relies on a wide range of ideas. Only after gathering as many perspectives as possible should you then begin reviewing and narrowing down to the best possible solution.

Step 4 – Make a decision

At this point, all viable solutions have to be analyzed before selecting the most appropriate one to implement. Picking the best possible solution depends on its ability to meet certain criteria. It must be suitable, feasible, and then acceptable.

What it means is that the solution must be able to get the problem solved. It should also be easy to see how such a solution fits into the equation. And then every member of the team involved in the brainstorming process has to unanimously accept the solution.

Read More : How to Network as a Software Engineer

Step 5 – Implement

After identifying and choosing the solution, the next logical step is to plan out the implementation process and then execute it. Coming up with a detailed plan is crucial if the solution is to be a success.

Now this plan must detail all the necessary steps required to implement the solution. It will also explain the length of time and stages of work required. Once all of that is put in place, you can then move forward with the execution. The idea is not just to execute a solution but to do it the right way.

Implementation using automated tests can help to keep unexpected issues from arising in the future. Some other problem-solving practices or approaches begin the process with this step. So, whenever any changes are made to the project, tests asserting that the changes will perform as required will be written first before the changes are then made.

Step 6 – Evaluate

No problem-solving process can be deemed comprehensive enough if there is no room for evaluation. Whatever the solution may be, it has to undergo strict evaluation in order to see how it performs. That will also help determine whether the problem still exists and the extent to which such an issue keeps recurring.

In the event that the problem persists despite the implementation of a detailed plan, then the developer and team may even have to restart the problem-solving process. However discouraging that may sound, at least you’ll have caught it early enough. And, this also proves the process worked.

Read More : How to Become a Software Engineer: Education, Steps & Tips for Success

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Final Thoughts

Developing problem-solving skills is quite necessary for software developers. To be a successful problem solver, you will need lots of years down the line to practice what you study.

Always remember that you are a problem solver first before anything else. There is more to building software than just understanding the tech behind it and writing lines of code. It’s all about improving your ability to identify problems and find solutions, and that will need lots of experience on your part.

Never shy away from problems, but learn to think critically and logically in any situation. By applying the six-step strategy for problem-solving at work discussed in this piece, you will be more equipped to come up with the most effective and efficient solutions.

We hope you enjoyed reading our guide on how to solve a problem as a software developer and ways to improve skills as a problem solver! If you have any questions, feedback, or other great problem-solving techniques or methods, let us know in the comments below 🙂

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How to solve coding problems

Tivadar Danka small portrait

Understanding math will make you a better engineer.

So, I am writing the best and most comprehensive book about it.

Every single line of code ever written was ultimately made with one purpose in mind: to solve problems. No matter what you do, you are solving problems on several scales at once.

A small one-liner solves a problem that makes a function work. The function is needed for a data processing pipeline. The pipeline is integrated into a platform that enables a machine learning-driven solution for its users.

Problems are everywhere. Their magnitude and impact might be different, but the general problem-solving strategies are the same.

As an engineer, developer, or data scientist, being effective in problem-solving can supercharge your results and put you before your peers. Some can do this instinctively after years of practice, and some have to put conscious effort to learn it. However, no matter who you are, you can, and you must improve your problem-solving skills.

Having a background in research-level mathematics, I had the opportunity to practice problem-solving and observe the process. Surprisingly, this is not something which you have to improvise each time. Rather, a successful problem-solver has several standard tools and a general plan under their belt, adapting as they go.

In this post, I aim to give an overview of these tools and use them to create a process you can follow at any time. Let's place ourselves into the following scenario to make the situation realistic: we are deep learning engineers working on an object detection model. Our data is limited, so we need to provide a solution for image augmentation .

Image augmentations. Source: albumentations README (https://github.com/albumentations-team/albumentations)

Augmentation generates new data from the available images by applying random transformations like crops, blurs, brightness changes, etc. See the figure above from the readme of the fantastic albumentations library.

You need to deliver the feature by next week, so you need to start working on it right away. How to approach the problem? (As a mathematician myself, my thinking process is heavily influenced by the book How to Solve It by George Pólya . Although a mathematical problem is different from real-life coding problems, this is a must-read for anyone who wishes to get good in problem-solving.)

Magnifying glass. Source: Unsplash (https://unsplash.com/photos/d9ILr-dbEdg)

Step 0: Understanding the problem

Before attempting to solve whatever problem you have in mind, some questions need to be answered. Not understanding the details can lead to wasted time, and you don't want that. For instance, it is good to be clear about the following.

  • What is the scale of the problem? In our image augmentation example, will you need to process thousands of images per second in production, or is it just for you to experiment with some methods? If a production-grade solution is required, you should be aware of this in advance.
  • Will other people use your solution? If people will work with your code extensively, you must put significant effort into code quality and documentation. However, you don't need to invest a lot of time if the solution is for your use only. (I already see people disagreeing with me :) However, I firmly believe in minimizing the amount of work. So, if you only need to quickly try out an idea and experiment, feel free not to consider code quality.)
  • Do you need a general or a special solution? You can waste a lot of time implementing features no one will ever use, including you. In our example, do you need a wide range of image augmentation methods or just vertical and horizontal flips? In the latter case, flipping the images in advance and adding them to your training set can also work, which requires minimal work.

A good gauge of your degree of understanding is your ability to explain and discuss the problem with others. Discussion is also a great way to discover unexpected approaches and edge cases.

When you understand your constraints and have a somewhat precise problem specification, it is time to get to work.

Step 1. Is there an existing solution?

The first thing you must always do is to look for existing solutions. Unless you are pushing the very boundaries of human knowledge, someone else had already encountered this issue, created a thread on Stack Overflow, and possibly wrote an open source library around it.

Take advantage of this. There are several benefits of using well-established tools instead of creating your own ones.

  • You save a tremendous amount of time and work. This is essential when operating under tight deadlines. (One of my teachers used to ironically say that "you can save an hour of Google search with two months of work". Spot on.)
  • Established tools are more likely to be correct. Open source tools are constantly validated and checked by the community, and thus, they are less likely to contain bugs. (Of course, this is not a guarantee.)
  • Less code for you to maintain. Again, we should always strive to reduce complexity, and preferably the amount of code. If you use an external tool, you don't have to worry about its maintenance, which is a great deal. Every line of code has a hidden cost of maintenance, to be paid later. (Often when it is the most inconvenient.)

Junior developers and data scientists often overlook these and prefer always to write everything from scratch. (I certainly did, but I quickly learned to know better.) The most extreme case I have seen was a developer who wrote his own deep learning framework. You should never do that unless you are a deep learning researcher and know how to do significantly better than the existing frameworks.

Of course, not all problems require an entire framework. Maybe you are just looking for a one-liner. Looking for existing solutions can be certainly beneficial, though you need to be careful in this case. Finding and using code snippets from Stack Overflow is only acceptable if you take the time to understand how and why it works. In the worst case, not doing so may result in unpleasant debugging sessions later or even severe security vulnerabilities.

For these smaller problems, looking for an existing solution consists of browsing tutorials and best practices. In general, there is a balance between ruthless pragmatism and outside-the-box thinking. When you implement something using established best practices, you are doing a favor for the developers who will use and maintain that piece of code. (Often including you.)

There is an existing solution. What next?

Suppose that you have followed my advice on your path towards delivering image augmentation for your data preprocessing pipeline, looked for existing solutions, and found the awesome albumentations library. Great! What next?

As always, there is a wide range of things to consider. Unfortunately, just because you have identified an external tool that can be a potential solution, it doesn't mean that it will suit your purposes.

  • Is it working well and supported properly? There is one thing worse than not using external code: using buggy and unmaintained external code. If a project is not well documented and not maintained, you should avoid it. For smaller problems, where answers generally can be found on Stack Overflow, the working well part is essential. (See the post I have linked above.)
  • Is it adaptable directly? For example, if you use an image processing library that is not compatible with albumentations, you must do additional work. Sometimes, this can be too much, and you have to look for another solution.
  • Does it perform adequately? If you need to process thousands of images per second, performance is a factor. A library might be convenient to use, but if it fails to perform, it has to go. This might not be a problem for all cases (for instance, if you are looking for a quick solution to do experiments), but if it is, it should be discovered early, before putting much work into it.
  • Do you understand how it works and what are its underlying assumptions? This is especially true for using Stack Overflow code snippets for the reasons I have mentioned above. For more complex issues like the image augmentation problem, you don't need to understand every piece of external code line by line. However, you need to be aware of the requirements of the library, for instance, the format of the input images.

This, of course, is applicable only if you can find an external solution. Read on to see what to do when this is not the case.

Teamwork. Source: Unsplash (https://unsplash.com/photos/qC2n6RQU4Vw)

What if there are no existing solutions?

Sometimes you have to develop a solution on your own. The smaller the problem is, the more frequently it happens. These are great opportunities for learning and building. This is the actual problem-solving part, the one that makes many of us most excited.

There are several strategies to employ, and all of them should be in your toolkit. If you read carefully, you'll notice that there is a common pattern.

  • Can you simplify? Sometimes, it is enough to solve only a special case. For instance, if you know that the inputs for your image augmentation pipeline will always have the same format, there is no need to spend time processing the input for several cases.
  • Isolate the components of the problem. Solving one problem can be difficult, let alone two at the same time. You should always make things easy for yourself. When I was younger, I thought that solving hard problems was The Way to get dev points. Soon, I have realized that the people who solve hard problems always do it by solving many small ones.
  • Can you solve for special cases? Before you go and implement an abstract interface for image augmentation, you should work on a single method to add into your pipeline. Once you discover the finer details and map out the exact requirements, you can devise a more general solution.

In essence, problem solving is an iterative process where you pick the problem apart step by step, eventually reducing it to easily solvable pieces.

Step 2. Break the solution (Optional)

There is a common trait which I have noticed in many excellent mathematicians and developers: they enjoy picking apart a solution, analyzing what makes them work. This is how you learn and how you build robust yet simple code.

Breaking things can be part of the problem solving process. Going from a special case to a general one, you usually discover solutions by breaking what you have.

Breaking things. Source: Unsplash (https://unsplash.com/photos/XZuqMUiSdgc)

When it is done

Depending on the magnitude of the problem itself, you should consider open-sourcing it if you are allowed. Solving problems for other developers is a great way to contribute to the community.

For instance, this is how I have built modAL , one of the most popular active learning libraries for Python. I started from a particular problem: building active learning pipelines for bioinformatics. Since building complex methods always require experimentation, I needed a tool that enabled rapid experimentation. This wasn't easy to achieve with the available frameworks at the time, so I slowly transformed my code into a tool that others could easily adopt.

What used to be "just" a solution became a library with thousands of users.

Contrary to popular belief, effective problem solving is not the same as coming up with brilliant ideas all the time. Instead, it is a thinking process with some well-defined and easy-to-use tools that anyone can learn. Smart developers use these instinctively, making them look like magic.

You can improve problem-solving skills with deliberate practice and awareness of thinking habits. There are several platforms where you can find problems to work on, like Project Euler or HackerRank . However, even if you start applying these methods to issues you encounter during your work, you'll see your skills improve rapidly.

Having a deep understanding of math will make you a better engineer.

I want to help you with this, so I am writing a comprehensive book that takes you from high school math to the advanced stuff. Join me on this journey and let's do this together!

Tutorial Playlist

Programming tutorial, your guide to the best backend languages for 2024, an ultimate guide that helps you to start learn coding 2024, what is backend development: the ultimate guide for beginners, all you need to know for choosing the first programming language to learn, here’s all you need to know about coding, decoding, and reasoning with examples, understanding what is xml: the best guide to xml and its concepts., an ultimate guide to learn the importance of low-code and no-code development, top frontend languages that you should know about, top 75+ frontend developer interview questions and answers, the ultimate guide to learn typescript generics, the most comprehensive guide for beginners to know ‘what is typescript’.

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Lesson 27 of 33 By Hemant Deshpande

An Ultimate Guide That Helps You to Develop and Improve Problem Solving in Programming

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!

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.

If you are looking to advance in your career. Simplilearn provides training and certification courses on various programming languages - Python , Java , Javascript , and many more. Check out our Post Graduate Program in Full Stack Web Development course that will help you excel in your career.

If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.

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About the author.

Hemant Deshpande

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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7-Step Approach to Solve Any Coding Problem (Important for Interviews)

Discover the comprehensive 7-step strategy to conquer coding problems in interviews. Learn how to understand, plan, code, debug, optimize, and present your solutions effectively.

Kishan Pandey

Kishan Pandey

The capability to approach hard problems confidently is a quality that can set you apart in the fast-paced world of coding interviews. Whether you're a seasoned programmer or just starting out, developing your problem-solving skills is imperative.

Welcome to a thorough manual that reveals a 7-step process for resolving any coding issue, a goldmine of knowledge created to help you conquer those technical interviews with grace. From understanding the problem to presenting your solution, we'll walk you through each step, equipping you with the knowledge and strategies needed to excel in the coding interview arena. Let's dive in and unlock the secrets to becoming a coding problem-solving virtuoso.

So, you’ve been given a problem in a coding interview for the company you have ever so wanted to be a part of, and for the same reason you feel nervous and you can’t find a way. You feel stuck, your lips drying and your palms sweating.

“But, I’ve solved such questions a hundred times before”. We know that. And that’s how coding can be, you’ve solved something 100 times but you can get stuck the 101st time. What do you do about it, then?

How can you ensure that you don’t falter in critical situations and solve those problems with impeccable consistency?

You can do that by clearing all the clutter and following a streamlined approach to solving problems. Now, there’s a 100% chance that you already follow a certain process subconsciously and get results out of it. But, in important moments your mind can get blurred and you might end up scratching your head.

Thus, it's important to have a process in your conscious mind, so, when the time comes you know what roadmap to take instead of feeling all fidgety.

And that’s why we have laid down a bullet-proof roadmap for you to approach any programming problem the right way and end up solving most problems at hand. It’ll help you in visualizing the solution and optimize for time and space complexity, not just in coding interviews but in general.

Make Sense of the Problem and Analyze

Before diving into coding, make sure you fully comprehend the problem statement. Break it down into simpler components and clarify any doubts. A deep understanding is the foundation for a successful solution.

It can be tempting to jump straight into coding and break that time barrier when given a problem. However, that’s the wrong approach more often than not.

Understanding the problem comes first and foremost. By understanding, we mean:

  • Making sure that you have enough information
  • Would you be able to explain the question to someone in a layman’s way?
  • Can you deduce what and how many inputs are required?
  • What would be the output for those inputs?

Remember, a war starts with strategy, not on the battlefield.

Clarify any sections of the problem that are unclear as you read through it. You can do this during an interview by asking the interviewer to describe the problem.

A doodle showing how to understand problems better

Incorporate system thinking into your problem-solving.

Systems thinking approach recognizes that a whole is greater than its parts — that all the aspects of a problem connect, interact, and influence results.

Have you ever encountered someone who sees things from a 10,000-foot perspective? They focus on the bigger picture rather than the specifics, and they are skilled at assessing situations before taking action. These people are most likely good "systems thinkers."

Visualize the problem using pen and paper

Consider different approaches to solving the problem. Choose the one that seems most efficient and scalable. Outline your solution on paper or in your mind, including algorithms and data structures.

Have you ever wondered why videos demonstrating the solutions to coding challenges often use diagrams and why coding interviews are typically conducted on whiteboards?

That’s because whiteboards allow you to draw diagrams which hugely benefits problem-solving.

Understanding how the internal state of a program changes is a significant aspect of coding, and diagrams are incredibly helpful tools for representing the internal state of data structures. Create a visual depiction of the problem and, if necessary, the internal states at each stage if you are having trouble understanding how the solution is reached.

The same can be true about code. The goal of handwriting code is to work through logic ahead of time. In design, there is a desire to "get in the browser" as quickly as possible, but there is a pearl of conventional wisdom in hand-drawing designs. A low-fidelity medium facilitates speedy experimentation and low-cost errors.

Formulating algorithm using pen and paper

If the input consists of trees, graphs, matrices, or linked lists, this technique is particularly helpful.

Break down the problem

Instead of being intimidated and becoming confused by a complex or large coding question, it is better to break it down into smaller pieces and then attempt to solve each component individually. The actions you should take to answer the challenging coding questions are listed below:

  • Create a UML diagram or a flowchart for the problem at hand
  • Separate the issue into smaller issues or parts
  • Address the smaller issues. Create separate functions for every subproblem
  • Call the solutions to each subproblem in the correct order or as necessary to connect them
  • Use classes and objects whenever necessary for handling questions (for real-world problems like management systems, etc.)

Breaking down a problem into sub problems

Use Sample Inputs and Examples thoroughly

Taking more examples and working through some sample inputs can help you grasp the issue better. It will also help you determine how many different scenarios your code should cover and what kinds of outputs or output ranges are possible.

Here is what we would recommend:

  • Use extremely basic examples to determine the results.
  • Increase the complexity and size of the inputs to determine the output and the desired number of use cases.

Now is the time to handle ‘edge cases’

  • Try solving the issue without any input; what should the result be?
  • Try the issue with invalid input and see what the result should be

Edge case- An edge case in programming often contains input values that require specific handling in an algorithm behind a computer program. In such circumstances, unit tests are typically created as a measure for validating the behaviour of computer programs; they test the boundary conditions of an algorithm, function, or method.

Depicting an edge case with Chess

Write the Pseudocode

Pseudocode is a high-level description of your solution using plain language. It helps you organize your thoughts before writing actual code. Ensure your pseudocode is clear and easy to understand.

The next step would be to write your devised plan in pseudo-code.

What’s a pseudo code?

Pseudocode is a straightforward explanation of an algorithm's steps. In other words, your pseudocode is your step-by-step strategy for resolving the issue.

Outline the steps you must take to resolve the issue in writing. There would be more steps if the issue were more complex.

For now, let’s take a simple problem as an example:

“Create a function that adds together two numbers and returns that value.”

For this problem, your pseudo code might go something like this-

  • // Create a sum variable.
  • Add the first input to the second input using the addition operator.
  • // Store values of both inputs into the sum variable.
  • // Return as output the sum variable.

This would be the step-by-step plan to reach the solution. Even if you encounter more complex cases, think of how a human solves problems systematically. What we often forget is that the working solution is just the code version of the manual approach we take.

You can create the code for an approach if you can identify a certain set of guidelines that apply to every example. Even while you might not find the best solution by doing this, it's a start and will earn you some points.

Start Coding

Start writing your code incrementally, focusing on small sections at a time. Test each part thoroughly to catch errors early. Use comments to explain complex logic and your thought process.

It's time to convert the pseudocode you just wrote into actual code.

Each line of your pseudocode should be replaced with actual code written in the language you are learning. If your difficulty has been broken down into smaller problems, note the code for each smaller problem. Keep these three things in mind when you write the code:

  • The point where you first started
  • Where are you at this moment?
  • What is your goal or intended outcome?

Also, keep in mind to focus on just the coding part at this step. You might think like- ‘What if it turns out to be inefficient code?’ But you don’t have to think of optimizing for now in case of a complex problem. You can always get to that later.

When participating in an interview, avoid wasting time by first finding out the entire solution before sharing it with the interviewer. Instead, keep the problem simple while sharing your strategy instead.

  • Describe your starting point to the interviewer.
  • Describe the strategy you have in mind right now.

You must have heard this in several places and by several experts that interviews are more interested in knowing your approach to the challenge. And that’s absolutely true.

If you find a part that is really difficult, ignore that for a while and continue solving the simpler sub-parts. This will give you the time to reflect on the challenging area; and finally, you can try applying a similar strategy to the challenging part. It works.

Optimize your code

Always strive to make your code better. Consider the past, reevaluate the situation, and look for a superior or different answer. You should aim to produce the proper quantity of good code, as we previously discussed, so always look for a different solution that is more effective than the original.

The final step you should take is not to just write the right answer to your problem. Write out the most effective or optimized solution for your code after thoroughly exploring all potential answers to the problem.

Code Optimization Technique

Following are some questions you should ask yourself once you have finished developing the solution to your code:

  • Does this code work for all input scenarios, including the edge cases?
  • Can there be a different approach to the same problem?
  • Is the coding productive? Can the performance be enhanced or made more effective?
  • How else could the code be made to be more readable?
  • Are there any additional stages or tasks you can eliminate?
  • Is your code repetitive in any way? Get rid of it.

And voila! You would have successfully solved the problem in most cases if you follow the process correctly.

Applying the same process in problem after problem will set a default method in your mind and coding will get much faster and more effective over time.

We hope you were able to grasp the step-by-step process of taking on a coding problem.

If you're an aspiring programmer striving to build a scalable career in the tech industry, check out our full-time and part-time courses in full-stack web development , offered at zero upfront fee.

And if you're already a working professional and want to elevate your career and salary to new heights, we've got you covered as well. Check out the Masai X - Backend Development Course and pay for the program only if you get placed for a CTC of INR 10 LPA.

What's the importance of understanding the problem before coding?

Understanding the problem is crucial because it forms the basis for any effective coding solution. Without a clear grasp of the problem statement, you may end up creating inefficient or incorrect code. Proper understanding allows you to break the problem into manageable components, select the right algorithms, and design an efficient solution.

Why is code optimization important in the problem-solving process?

Code optimization is essential to enhance the efficiency and performance of your solution. In interviews, interviewers often evaluate not just the correctness of your code but also its efficiency. Optimized code can reduce time and space complexity, making it more scalable and effective. It showcases your ability to think critically and produce high-quality code, which can be a significant advantage during technical interviews.

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Coding is a powerful skill, one that is becoming increasingly important in today's technology-driven world. It's like a universal language, enabling you to build applications, websites, and software that can impact millions of lives. However, mastering the art of coding is not just about learning different programming languages or memorizing algorithms. It is, at its core, about problem-solving. Every line of code you write is part of a solution to a problem, whether it's a simple issue or a complex business challenge. This blog post aims to guide beginners on how to master the art of problem-solving in coding. We'll delve into the mindset required, explore the steps to approach a problem, and use examples to understand how to apply these skills effectively.

  • The Mindset for Problem-Solving in Coding

Before we start looking into the specifics of problem-solving techniques, we must first understand the mindset required. When it comes to programming, having the right mindset is half the battle.

Embrace the Challenge

Coding is all about challenges. Sometimes, you'll face problems that seem impossible to solve. In such instances, remember that every problem has a solution. Your job as a programmer is to find that solution. Embrace the challenge instead of running away from it.

Understand that Failure is a Step Towards Success

You'll often write code that doesn't work as expected. That's completely fine! Failure is a part of the learning process. Each failed attempt provides insights into what doesn't work, pushing you closer to a solution.

Patience is a virtue in programming. Some problems require a lot of time to solve. You may need to try multiple solutions before you find the right one. Don't rush. Take your time to understand the problem and its possible solutions.

  • Approaching a Coding Problem

With the right mindset, you're ready to approach any coding problem. Here's a step-by-step guide to tackle it:

Step 1: Understand the Problem

Before you start writing any code, ensure that you understand the problem fully. Read the problem statement carefully. Identify the inputs and expected outputs. Try to understand the constraints and edge cases.

For example, let's consider a simple problem: Write a function in Python to calculate the factorial of a number.

In this case, the input is a number ( n ) and the expected output is the factorial of that number.

Step 2: Break Down the Problem

Once you understand the problem, break it down into smaller, manageable sub-problems. This process is also known as "decomposition."

For the factorial problem, it can be broken down into:

  • If the number is 0 or 1, return 1.
  • If the number is positive, multiply it with the factorial of the number minus one.

Step 3: Plan Your Solution

Next, plan how to solve each sub-problem. This could involve deciding on the algorithms to use or the data structures that might help solve the problem effectively. For our factorial problem, we'll use recursion to solve the problem.

Step 4: Write Code

With the plan ready, start writing your code. Focus on one sub-problem at a time. Ensure your code is clean and readable. Don't worry about optimization at this point.

Here's how we might write the code for our factorial problem:

Step 5: Test Your Code

Once you've written the code, it's time to test it. Start by using the test cases provided with the problem.Then, think of additional test cases, especially edge cases that might break your code. For our factorial function, we might test with n=5 , n=0 , and n=-1 .

Step 6: Refine Your Solution

After testing, you might find parts of your code that could be optimized or made more readable. This step is known as "refactoring." It involves improving your code without changing its external behavior. For our factorial function, there's not much to refine, but in more complex problems, you might find opportunities to make your code cleaner or more efficient.

  • Common Techniques for Problem Solving

Knowing some common techniques for problem-solving can also be beneficial. Here are a few that you should be familiar with:

1. Brute Force

The brute force approach involves trying every possible solution until you find the right one. It's usually not efficient, but it can be helpful when the problem space is small, or when you just need a starting point.

2. Divide and Conquer

In divide and conquer, you break the problem down into smaller sub-problems, solve each independently, and then combine their solutions to solve the original problem. We've used this approach in the factorial example, where we broke down the calculation into a series of smaller calculations.

3. Greedy Algorithms

Greedy algorithms involve making the optimal choice at each decision point in the hope that these local optimums will lead to a global optimum. These algorithms are useful when the problem has an optimal substructure, meaning an optimal solution can be constructed efficiently from optimal solutions of its subproblems.

4. Dynamic Programming

Dynamic programming involves breaking down a problem into simpler sub-problems, solving each just once, and storing their solutions – ideally in a table structure. If the same sub-problem occurs, instead of recomputing its solution, one simply looks up the previously computed solution, thereby saving computation time. This technique is used when the problem has overlapping subproblems.

1. How can I improve my coding problem-solving skills?

Practice is key to improving your coding problem-solving skills. Websites like LeetCode, HackerRank, and CodeSignal offer a vast range of problems that can help you hone your skills. Additionally, learning about different algorithms and data structures can give you a toolbox of methods to approach problems.

2. What language should I use for coding problem-solving?

The language you use for problem-solving depends on your comfort level with the language and sometimes, the problem itself. Some languages like Python are often recommended for beginners due to their simplicity, but the concepts of problem-solving remain the same across all languages.

3. How do I handle a problem that I can't solve?

If you encounter a problem that you can't solve, try breaking it down into smaller parts, and focus on solving those first. If you're still stuck, don't hesitate to search for help. Websites like StackOverflow have vast communities of programmers who are willing to help. Lastly, stepping away from the problem for a while can also help. Often, solutions come to mind when you're not actively thinking about the problem.

4. I understand the theory of problem-solving, but I struggle to implement it in code. What should I do?

Coding is a skill that improves with practice. If you understand the theory, you're halfway there. Try to write the code for the problem, even if you struggle. With time, you'll find it easierto translate your thoughts into code. Don't shy away from seeking help or looking at other people's code to understand different ways to approach a problem.

5. Is there a 'best' way to solve a problem?

Not necessarily. Often, there are multiple ways to solve a problem in coding. Some solutions might be more efficient, readable, or elegant than others, but the 'best' solution can depend on a variety of factors, including the specific requirements of the problem, the constraints of your environment, or even your personal coding style.

6. What are the common mistakes beginners make while problem-solving in coding?

Some common mistakes include not fully understanding the problem before starting to code, not considering edge cases, focusing on optimization too early in the process, and not testing the code thoroughly. It's important to develop a systematic approach to problem-solving to avoid these pitfalls.

7. How important is understanding algorithms in coding problem-solving?

Understanding algorithms is a crucial part of problem-solving in coding. An algorithm is like a recipe; it's a set of step-by-step instructions to solve a problem. Knowing a wide range of algorithms allows you to choose the most appropriate one for the problem at hand. It's not just about memorizing algorithms, but understanding their logic, their pros and cons, and when to use which one.

Remember, coding is as much an art as it is a science. Developing the mindset of a problem solver and practicing the systematic approach to solving coding problems is a continuous journey. Don't rush it. Enjoy each problem you solve, learn from your mistakes, and celebrate your progress. The skills you'll gain will not only make you a better programmer but a better thinker overall.

And with that, we come to the end of this beginner-friendly guide to mastering the art of coding problem-solving. We hope you found it useful and informative. Happy coding!

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How Coding Can Help You Master Problem Solving

  • Published: April 26, 2023

In today’s rapidly evolving technological landscape, problem solving skills have become increasingly valuable. One of the most effective ways to develop and enhance problem solving abilities is through learning to code. Coding, or computer programming, involves creating algorithms, writing code, and debugging software to create functional and efficient programs. In this article, we will explore the ways in which coding can improve problem solving skills and foster the development of logical and analytical thinking.

Enhancing Problem solving Skills through Coding

Coding is one of the skills that can help children with problem solving. With coding, children can approach problems creatively and innovatively, and enhance their problem solving skills. Here is some ways in which coding can improve problem solving skills:

Coding for kids

Breaking down complex problems into manageable tasks

Coding requires programmers to break down complex problems into smaller, more manageable tasks. This process of decomposition is a vital aspect of problem solving, as it enables individuals to approach large, seemingly insurmountable challenges by dividing them into a series of smaller, more easily solvable problems. As a result, coding can help individuals develop the ability to think systematically and strategically when faced with complex problems in various aspects of life.

Unlocking the Power of Computational Thinking

At the core of coding lies the necessity for logical and analytical thinking. Programmers must understand the relationships between different elements of a problem, analyze data, and apply logical reasoning to design effective algorithms. By engaging in the process of coding, individuals can strengthen their logical and analytical thinking skills, making them better equipped to tackle problems across various disciplines.

Debugging and iterative problem solving

One of the most essential aspects of coding is debugging, which involves identifying and fixing errors in the code. Debugging requires a methodical and iterative approach to problem solving , as programmers must test their code, identify issues, and refine their solutions until they achieve the desired outcome. Through this process, coders develop persistence, resilience, and the ability to learn from their mistakes – all of which are valuable problem solving skills.

Problem solving

Pattern recognition and abstraction

Coding often involves identifying patterns and creating abstract representations of problems to simplify and streamline the problem solving process. Recognizing patterns can help individuals see connections between seemingly unrelated concepts, enabling them to develop innovative solutions to problems. By engaging in coding activities, individuals can improve their pattern recognition and abstraction skills, enhancing their ability to solve complex problems across various domains.

Algorithmic thinking

Algorithmic thinking is the ability to design and implement a structured set of instructions to solve a problem. It is a crucial component of coding and involves identifying the most efficient and effective method to solve a given problem. Developing strong algorithmic thinking skills can help individuals become more effective problem solvers, as they learn to approach problems in a systematic and organized manner.

Collaboration and teamwork

Coding often involves working in teams, where individuals must collaborate and communicate effectively to solve problems. By working together, programmers can leverage the diverse perspectives and expertise of their teammates to develop innovative solutions to complex challenges. Through this collaborative process, individuals can improve their problem solving skills, learning to navigate interpersonal dynamics and capitalize on the collective intelligence of their team.

problem solving process coding

Example: Solving a real-world problem through coding

Imagine a group of students participating in a coding competition, where they are tasked with developing an app that helps users find and sort local recycling centers based on the types of materials accepted. To tackle this problem, the students must apply their problem-solving skills throughout the entire process:

  • Breaking down the problem: The students divide the challenge into smaller tasks, such as collecting recycling center data, designing the user interface, creating sorting algorithms, and implementing geolocation functionality.
  • Logical and analytical thinking: The students analyze the available data and apply logical reasoning to determine the most effective way to sort and present the recycling centers to the user.
  • Debugging and iterative problem-solving: As the students develop their app, they will undoubtedly encounter bugs and errors. They must test, debug, and refine their code until it works as intended, showcasing their persistence and resilience.
  • Pattern recognition and abstraction: The students may notice patterns in the data, such as common combinations of materials accepted at recycling centers. They can use this information to create abstract categories or filters, simplifying the user experience and streamlining the sorting process.
  • Algorithmic thinking: The students need to design algorithms for sorting the recycling centers based on various factors, such as distance, materials accepted, or user ratings. By doing so, they develop their algorithmic thinking skills.
  • Collaboration and teamwork: Working together on this project, the students must communicate effectively, delegate tasks, and learn from each other to achieve their goal.

In conclusion, learning to code offers numerous benefits in terms of improving problem-solving skills. From breaking down complex problems into manageable tasks to fostering logical and analytical thinking, coding can help individuals develop a more comprehensive and versatile approach to problem-solving.

What is The Benefits of Coding?

With Codiska , kids can learn to code in a fun and interactive way, while developing problem-solving, critical thinking, and mathematical skills. By engaging in coding activities, such as the recycling center app project example, individuals can strengthen their problem-solving abilities, making them better equipped to tackle challenges in various aspects of their personal and professional lives.

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Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

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A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio

February 15, 2024

problem solving process coding

Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription . The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio .

Today, we’re also excited to introduce our next-generation Gemini 1.5 model , which uses a new Mixture-of-Experts (MoE) approach to improve efficiency. It routes your request to a group of smaller "expert” neural networks so responses are faster and higher quality.

Developers can sign up for our Private Preview of Gemini 1.5 Pro , our mid-sized multimodal model optimized for scaling across a wide-range of tasks. The model features a new, experimental 1 million token context window, and will be available to try out in  Google AI Studio . Google AI Studio is the fastest way to build with Gemini models and enables developers to easily integrate the Gemini API in their applications. It’s available in 38 languages across 180+ countries and territories .

1,000,000 tokens: Unlocking new use cases for developers

Before today, the largest context window in the world for a publicly available large language model was 200,000 tokens. We’ve been able to significantly increase this — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model. Gemini 1.5 Pro will come with a 128,000 token context window by default, but today’s Private Preview will have access to the experimental 1 million token context window.

We’re excited about the new possibilities that larger context windows enable. You can directly upload large PDFs, code repositories, or even lengthy videos as prompts in Google AI Studio. Gemini 1.5 Pro will then reason across modalities and output text.

Upload multiple files and ask questions We’ve added the ability for developers to upload multiple files, like PDFs, and ask questions in Google AI Studio. The larger context window allows the model to take in more information — making the output more consistent, relevant and useful. With this 1 million token context window, we’ve been able to load in over 700,000 words of text in one go. Gemini 1.5 Pro can find and reason from particular quotes across the Apollo 11 PDF transcript. 
[Video sped up for demo purposes]
Query an entire code repository The large context window also enables a deep analysis of an entire codebase, helping Gemini models grasp complex relationships, patterns, and understanding of code. A developer could upload a new codebase directly from their computer or via Google Drive, and use the model to onboard quickly and gain an understanding of the code. Gemini 1.5 Pro can help developers boost productivity when learning a new codebase.  
Add a full length video Gemini 1.5 Pro can also reason across up to 1 hour of video. When you attach a video, Google AI Studio breaks it down into thousands of frames (without audio), and then you can perform highly sophisticated reasoning and problem-solving tasks since the Gemini models are multimodal. Gemini 1.5 Pro can perform reasoning and problem-solving tasks across video and other visual inputs.  

More ways for developers to build with Gemini models

In addition to bringing you the latest model innovations, we’re also making it easier for you to build with Gemini:

Easy tuning. Provide a set of examples, and you can customize Gemini for your specific needs in minutes from inside Google AI Studio. This feature rolls out in the next few days. 
New developer surfaces . Integrate the Gemini API to build new AI-powered features today with new Firebase Extensions , across your development workspace in Project IDX , or with our newly released Google AI Dart SDK . 
Lower pricing for Gemini 1.0 Pro . We’re also updating the 1.0 Pro model, which offers a good balance of cost and performance for many AI tasks. Today’s stable version is priced 50% less for text inputs and 25% less for outputs than previously announced. The upcoming pay-as-you-go plans for AI Studio are coming soon.

Since December, developers of all sizes have been building with Gemini models, and we’re excited to turn cutting edge research into early developer products in Google AI Studio . Expect some latency in this preview version due to the experimental nature of the large context window feature, but we’re excited to start a phased rollout as we continue to fine-tune the model and get your feedback. We hope you enjoy experimenting with it early on, like we have.

IMAGES

  1. PROBLEM SOLVING

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  2. Solve Coding Problem With a Simple Four Step Process • CS Code

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  3. how to learn problem solving coding

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  4. 6 steps to help you solve your computer science and coding problems If

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  5. How to Solve Coding Problems with a Simple Four Step Method

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  6. Problem Solving

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VIDEO

  1. Problem Identification for Project

  2. Problem Solving Process

  3. Tutorial problem

  4. Tutorial problem

  5. Algorithms: problem to code to analysis

  6. Problem Solving Process. Part

COMMENTS

  1. How to Solve Coding Problems with a Simple Four Step Method

    Step 1: Understand the problem. When given a coding problem in an interview, it's tempting to rush into coding. This is hard to avoid, especially if you have a time limit. However, try to resist this urge. Make sure you actually understand the problem before you get started with solving it. Read through the problem.

  2. What is Problem Solving? An Introduction

    Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow.

  3. How To Approach A Coding Problem

    These steps you need to follow while solving a problem: - Understand the question, read it 2-3 times. - Take an estimate of the required complexity. - find, edge cases based on the constraints. - find a brute-force solution. ensure it will pass. - Optimize code, ensure, and repeat this step.

  4. Problem Solving

    Problem solving is writing an original program that performs a particular set of tasks and meets all stated constraints. The set of tasks can range from solving small coding exercises all the way up to building a social network site like Facebook or a search engine like Google.

  5. The Problem Solving Process

    45 minutes Overview Question of the Day: What are some common steps we can use to solve many different types of problems? This lesson introduces the formal problem solving process that students will use over the course of the year, Define - Prepare - Try - Reflect.

  6. How to solve coding problems step by step

    The first and most important step in solving any coding problem is to understand the problem statement. Read the instructions carefully and make sure you fully comprehend the requirements. Break down the problem into smaller, manageable tasks and try to visualize the expected input and output. Choose the Right Data Structure and Algorithm

  7. The Engineering Method: A Step-by-Step Process for Solving ...

    Step 3: Choose a solution. A big part of engineering is making decisions and choosing between different implementation options. Proactively point out the advantages/disadvantages of your ideas. If there is more than one solution on the table, you should consider which makes the most sense build.

  8. The 5 Pillars of Complex Problem Solving with Code

    Oct 1, 2019 -- 2 After spending hundreds of hours helping people take their first steps with code, I developed a five part model for problem solving. In fact, it's even easier than that, as it really breaks down to three core skills, and two processes. In this piece I'll explain each of them and how they interreact. Problem Decomposition

  9. Sequencing, selection, and iteration

    An algorithm is made up of three basic building blocks: sequencing, selection, and iteration. Sequencing: An algorithm is a step-by-step process, and the order of those steps are crucial to ensuring the correctness of an algorithm. Here's an algorithm for translating a word into Pig Latin, like from "pig" to "ig-pay": 1.

  10. How to think like a programmer

    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.

  11. What is Pseudocode? How to Use Pseudocode to Solve Coding Problems

    This is arguably the most important step in the process. If you fail to properly understand the question, you won't be able to work through the problem and figure out the possible steps to take. Once you identify the main problem to be solved you'll be ready to tackle it. Step 3: Break the problem down.

  12. How to Solve Coding Challenges: A Comprehensive Guide

    This comprehensive guide will walk you through a step-by-step approach to solving coding challenges. It helped me prepare for the coding interviews and land several jobs in my dev career. 1. Understand the Problem. The first and most crucial step in solving any coding challenge is thoroughly understanding the problem statement.

  13. Problem-Solving Skills for Software Developers: Why & How to Improve

    To train the problem-solving side of your brain, these four tips and strategies can help you improve your abilities: 1. Make problem-solving a part of your life. Never restrict yourself to working on problems only during work hours. Don't make it a chore, but, instead, do things that make problem-solving look fun.

  14. How to solve coding problems

    Tivadar Danka Every single line of code ever written was ultimately made with one purpose in mind: to solve problems. No matter what you do, you are solving problems on several scales at once. A small one-liner solves a problem that makes a function work. The function is needed for a data processing pipeline.

  15. How to Develop Problem Solving Skills in Programming

    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.

  16. Computer Science Discoveries

    Use a problem-solving process to tackle puzzles, challenges, and real-world scenarios. Learn about computer input, output, storage, and processing to aid in problem-solving. ... This enables learners to seamlessly transition from block-based coding to text-based languages, and fosters creativity and innovation. Try out programming tools

  17. 7-Step Approach to Solve Any Coding Problem (Important for Interviews)

    For this problem, your pseudo code might go something like this-. // Create a sum variable. Add the first input to the second input using the addition operator. // Store values of both inputs into the sum variable. // Return as output the sum variable. This would be the step-by-step plan to reach the solution.

  18. Mastering the Art of Coding Problem-Solving

    Step 5: Test Your Code. Once you've written the code, it's time to test it. Start by using the test cases provided with the problem.Then, think of additional test cases, especially edge cases that might break your code. For our factorial function, we might test with n=5, n=0, and n=-1.

  19. Online Coding Practice Problems & Challenges

    33 Problems Beginner level Practice Rust Get hands-on experience in Rust language with this interactive and practical course. Master the basic syntax of the language and develop real-world applications with confidence. 33 Problems Beginner level Practice C# Learn the basics of C# programming with ease in this interactive and practical course.

  20. How Coding Can Help You Master Problem Solving

    One of the most effective ways to develop and enhance problem solving abilities is through learning to code. Coding, or computer programming, involves creating algorithms, writing code, and debugging software to create functional and efficient programs. In this article, we will explore the ways in which coding can improve problem solving skills ...

  21. How To Do Process Coding

    Codes will typically be gerunds that end with "ing". Process coding isn't meant to be a standalone method of coding, but just one technique to use along with other coding techniques. Read our guide and example of process coding ... When researching a topic involving people reaching a goal or solving a problem. When capturing the sequence ...

  22. What is Problem Solving? Steps, Process & Techniques

    1. Define the problem Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes. The sections below help explain key problem-solving steps.

  23. THE PROBLEM-SOLVING PROCESS Flashcards

    Step 1: Define the Problem. Differentiate fact from opinion. Specify underlying causes. Consult each faction involved for information. State the problem specifically. Identify what standard or expectation is violated. Determine in which process the problem lies. Avoid trying to solve the problem without data.

  24. Introducing Gemini 1.5, Google's next-generation AI model

    This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. ... 1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better ...

  25. Gemini 1.5: Our next-generation model, now available for Private

    Posted by Jaclyn Konzelmann and Wiktor Gworek - Google Labs. Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription.The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio.

  26. Datascience

    14 likes, 0 comments - datascience_mind on February 21, 2024: "Let's embark on our rocket journey of preparation in just 2 days! ️ Day 1: Mastering t..."