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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 Fizz Buzz

Understanding the problem

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

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

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

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

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

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

Writing the pseudocode

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

Dividing and conquering

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Knowledge check

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

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

Additional resources

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

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

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Lesson 27 of 34 By Hemant Deshpande

Table of Contents

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

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Topics covered in this problem solving in programming article are:

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

What is Problem Solving in Programming?

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

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

How Does It Impact Your Career?

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

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

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

Problem Solving Skills in Programming

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

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

Steps Involved in Problem Solving

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

Basically, they are divided into four categories:

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

Analysing the Problem

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

Developing the Algorithm

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

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

Testing and Debugging

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

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

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

Steps to Improve Problem Solving in Programming

Right mindset.

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

Making Right Decisions

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

Keeping Ideas on Track

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

Learning from Feedbacks

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

Asking Questions

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

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

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

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

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

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

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

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

Interview Cake

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

HackerEarth

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

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

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

2. Code Refactoring

3. using data structures & algorithms.

4. Using Version Control Systems

Expert advice

UNIT 1: How to Think Like an Engineer.

Learning objectives.

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

Introduction

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

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

Computational Thinking

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

An Example Algorithm

Let us look at a very simple algorithm called find_max.

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

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

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

Algorithm :

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

Does this meet the criteria for being an algorithm?

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

[3] Figure 3: Example Algotithm

Verifying your Algorithm

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

Figure 4 “ Keyboard ” by Geralt is licensed under CC 2

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

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

Good (effective) test cases:

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

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

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

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

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

Figure 5: Process of Computational Problem Solving

Values and Variables

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

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

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

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

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

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

What is a program?

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

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

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

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

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

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

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

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

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

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

Computational Problem Design using the Basic Programming Constructs

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

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

Figure 7: the 3 Programming Constructs

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

Sequential Control Example

The following algorithm is an example of sequential control .

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

Inputs : Two numbers.

Outputs : The sum and the product.

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

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

Selection Control Examples

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

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

Inputs : Two numbers and calculation option.

Outputs : The value of the chosen calculation.

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

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

> is greater than

< is less than

>= is greater than or equal

<= is less than or equal

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

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

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

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

Inputs : One positive integer number.

Outputs : The calculated tax amount.

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

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

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

Iterative Control Examples

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

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

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

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

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

Inputs : A series of individual characters.

Outputs : Each character typed in by the user.

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

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

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

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

Outputs : Each word typed in by the user.

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

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

The Role of Programming in the Field of Informatics

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

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

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

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

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

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

Unit Summary

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

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

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

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

Practice Problems

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

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

• Write an algorithm which repeatedly accepts numbers until the user enters “done”. Once “done” is entered, display the total sum of all the numbers, the count of numbers entered, and the average of all the numbers.
• Write an algorithm that sums a series of ten positive integers entered by the user excluding all numbers greater than 100. Display the final sum.
• Wing, Jeannette M. "Computational thinking." Communications of the ACM 49.3 (2006): 33-35. ↵

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11 Websites To Practice You Coding And Your Problem Solving Skills

11 websites to practice your coding and problem-solving skills.

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

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

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

1. TopCoder

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

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

2. Coderbyte

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

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

3. Project Euler

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

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

4. HackerRank

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

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

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

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

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

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

5. CodeChef

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

6. Exercism.io

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

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

7. Codewars

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

8. LeetCode

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

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

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

10. CodinGame

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

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

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

CEO & Founder at Coderbyte.

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The Most Important Soft Skill for Developers & How to Get Better at It

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At its core, programming is just solving problems so a computer can execute a task. Or, as one of our engineers Nick Duckwiler aptly put it: “A lot of engineering is just solving headaches.” Indeed, between fixing bugs and dreaming up app ideas that can address real world difficulties, devs need to be enthusiastic about solving problems of all sizes.   

On top of all the technical knowledge that’s required for engineering roles, you also should work on soft skills, which are personal attributes that enable you to work well with others. Problem solving is one of the most essential soft skills to have in technical positions , and luckily, there are plenty of ways to get better at tackling challenges and finding solutions.

Our course catalog just got a major update with over 70 new courses that cover professional or soft skills, like communication, leadership, productivity, and teamwork. These courses are completely free and can help you unlock essential skills for your career. In the free course Becoming a Successful Collaborator , you’ll master the meaning of collaboration, effective teaming practices, and conflict management styles, so you can enhance problem-solving, productivity, and team interconnection. Read on for more creative proven problem-solving tactics that you can try today.

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Write out the problem

Your problem won’t always come right out and say: “It’s me, hi. I’m the problem , it’s me.” In fact, something that often gets in the way of solving a problem is that we zero in on the wrong problem.

When pinpointing a problem, you can try borrowing a UX research technique that’s part of the design thinking process. After you’ve done some initial research or information gathering, you delineate your problem space and write a problem statement, which is a concise couple of sentences that succinctly define the task and offer a clear sense of direction. Write out the who, what, where, when, and why of your problem.

Getting to the core of your fundamental issue will make addressing the symptoms much easier. You can learn more about this strategy in our free course Learn Design Thinking: Ideation .

Don’t try to solve it alone

Rather than spinning your wheels trying to fix a problem on your own, consider having other people weigh in. Set up a brainstorming session for the problem you’re trying to solve, see if anyone can pair program with you, or send a Slack message to your team and see what your collective intelligence can accomplish. In the free course Expanding Your Communication Skill Set , you’ll learn how to collaborate and get things done in all kinds of workplace scenarios.

It’s easy to get tunnel vision when you’re working on a project and become fixated on one part of it. Getting more people involved in the problem-solving process will enable you to address blind spots, consider fresh perspectives, and ultimately get valuable feedback and validation for your idea. Not to mention, you’ll get experience collaborating with other people, which is a soft skill in and of itself.

Say it out loud

Ever seen a rubber duck on a programmer’s desk and wondered what it’s doing there? There’s a popular debugging technique called “ rubberducking ,” where you describe out loud what your code is supposed to do to the duck. As you verbally articulate your code and thoughts to the silent, non-judgmental duck, you may identify issues or problems that you skipped over before. Though you might have to work up the courage to talk to an inanimate object at your desk, you’ll be surprised how effective and practical rubberducking can be when it comes to pinpointing a problem.

See how other people approached the problem

Remember: You’re probably not the first person to have experienced this problem. There’s a plethora of resources that developers use to ask questions, get feedback, or crowd-source solutions for bugs. Go to Stack Overflow and see if someone else has experienced your issue and created a workaround. Or look through Docs , our open-contribution code documentation for popular languages, to see if you can find a solution. (Better yet, once you figure your issue out, you could take what you learned and contribute a Doc for folks to reference in the future.)

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Our professional skills courses are carefully selected by our team to offer the most relevant and in-demand business skills for learners like you. You can begin learning immediately — all you need is a free Codecademy account to get started.

This blog was originally published in October 2023 and has been updated to include details about our new professional skills courses.

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10 Python Code Challenges to Try During Pride

Celebrate Pride with these bite-sized Python coding challenges.

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I’m taking a course that teaches Python, and while it’s only been a couple of weeks, it seems like my classmates already have a good grasp of what to do. They can easily write code from scratch even if they haven’t had programming experience before, while I usually have to look up similar problems and see how to get the answer from that. I do try on my own first and look through the textbook, and I understand the concepts individually just fine, but putting them all together gives me a hard time for some reason. I know we shouldn’t compare ourselves to others but it’s been kind of hard to not do that lately. I’m constantly asking them for help any chance I get, but when it comes to big projects and exams, I won’t be able to ask for help and would like to do my assignments independently. Any tips/advice? Thanks in advance.

If you have experience with object-oriented programming languages, then you’ve likely heard of the SOLID principles, MVP, singleton, factory, and observer patterns. Our new e-book highlights best practices for using these principles and patterns to create scalable game code architecture in your Unity project.

For every software design issue you encounter, a thousand developers have been there before. Though you can’t always ask them directly for advice, you can learn from their decisions through design patterns.

By implementing common, game programming design patterns in your Unity project, you can efficiently build and maintain a clean, organized, and readable codebase, which in turn, creates a solid foundation for scaling your game, development team, and business.

In our community, we often hear that it can be intimidating to learn how to incorporate design patterns and principles, such as SOLID and KISS, into daily development. That’s why our free e-book, Level up your code with game programming patterns , explains well-known design patterns and shares practical examples for using them in your Unity project.

Written by internal and external Unity experts, the e-book is a resource that can help expand your developer’s toolbox and accelerate your project’s success. Read on for a preview of what the guide entails.

Design patterns are general solutions to common problems found in software engineering. These aren’t finished solutions you can copy and paste into your code, but extra tools that can help you build larger, scalable applications when used correctly.

By integrating patterns consistently into your project, you can improve code readability and make your codebase cleaner. Design patterns not only reduce refactoring and the time spent testing, they speed up onboarding and development processes.

However, every design pattern comes with tradeoffs, whether that means additional structures to maintain or more setup at the beginning. You’ll need to do a cost-benefit assessment to determine if the advantage justifies the extra work required. Of course, this assessment will vary based on your project.

KISS stands for “keep it simple, stupid.” The aim of this principle is to avoid unnecessary complexity in a system, as simplicity helps drive greater levels of user acceptance and interaction.

Note that “simple” does not equate to “easy.” Making something simple means making it focused. While you can create the same functionality without the patterns (and often more quickly), something fast and easy doesn’t necessarily result in something simple.

If you’re unsure whether a pattern applies to your particular issue, you might hold off until it feels like a more natural fit. Don’t use a pattern because it’s new or novel to you. Use it when you need it.

It’s in this spirit that the e-book was created. Keep the guide handy as a source of inspiration for new ways of organizing your code – not as a strict set of rules for you to follow.

Now, let’s turn to some of the key software design principles.

SOLID is a mnemonic acronym for five core fundamentals of software design. You can think of them as five basic rules to keep in mind while coding, to ensure that object-oriented designs remain flexible and maintainable.

The SOLID principles were first introduced by Robert C. Martin in the paper, Design Principles and Design Patterns . First published in 2000, the principles described are still applicable today, and to C# scripting in Unity:

• Single responsibility states that each module, class, or function is responsible for one thing and encapsulates only that part of the logic.
• Open-closed states that classes must be open for extension but closed for modification; that means structuring your classes to create new behavior without modifying the original code.
• Liskov substitution states that derived classes must be substitutable for their base class when using inheritance.
• Interface segregation states that no client should be forced to depend on methods it does not use. Clients should only implement what they need.
• Dependency inversion states that high-level modules should not import anything directly from low-level modules. Both should depend on abstractions.

In the e-book, we provide illustrated examples of each principle with clear explanations for using them in Unity. In some cases, adhering to SOLID can result in additional work up front. You may need to refactor some of your functionality into abstractions or interfaces, but there is often a payoff in long-term savings.

The principles have dominated software design for nearly two decades at the enterprise level because they’re so well-suited to large applications that scale. If you’re unsure about how to use them, refer back to the KISS principle. Keep it simple, and don’t try to force the principles into your scripts just for the sake of doing so. Let them organically work themselves into place through necessity.

If you’re interested in learning more, check out the SOLID presentation from Unite Austin 2017 by Dan Sagmiller of Productive Edge.

What’s the difference between a design principle and a design pattern? One way to answer that question is to consider SOLID as a framework for, or a foundational approach to, writing object-oriented code. While design patterns are solutions or tools you can implement to avoid everyday software problems, remember that they’re not off-the-shelf recipes – or for that matter, algorithms with specific steps for achieving specific results.

A design pattern can be thought of as a blueprint. It’s a general plan that leaves the actual construction up to you. For instance, two programs can follow the same pattern but involve very different code.

When developers encounter the same problem in the wild, many of them will inevitably come up with similar solutions. Once a solution is repeated enough times, someone might “discover” a pattern and formally give it a name.

Many of today’s software design patterns stem from the seminal work, Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. This book unpacks 23 such patterns identified in a variety of day-to-day applications.

The original authors are often referred to as the “Gang of Four” (GoF),and you’ll also hear the original patterns dubbed the GoF patterns. While the examples cited are mostly in C++ (and Smalltalk), you can apply their ideas to any object-oriented language, such as C#.

Since the Gang of Four originally published Design Patterns in 1994, developers have since established dozens more object-oriented patterns in a variety of fields, including game development.

While you can work as a game programmer without studying design patterns, learning them will help you become a better developer. After all, design patterns are labeled as such because they’re common solutions to well-known problems.

Software engineers rediscover them all the time in the normal course of development. You may have already implemented some of these patterns unwittingly.

Train yourself to look for them. Doing this can help you:

• Learn object-oriented programming : Design patterns aren’t secrets buried in an esoteric StackOverflow post. They are common ways to overcome everyday hurdles in development. They can inform you of how many other developers have approached the same issue – remember, even if you’re not using patterns, someone else is.
• Talk to other developers : Patterns can serve as a shorthand when trying to communicate as a team. Mention the “command pattern” or “object pool” and experienced Unity developers will know what you’re trying to implement.
• Explore new frameworks :When you import a built-in package or something from the Asset Store, inevitably you’ll stumble onto one or more patterns discussed here. Recognizing design patterns will help you understand how a new framework operates, as well as the thought process involved in its creation.

As indicated earlier, not all design patterns apply to every game application. Don’t go looking for them with Maslow’s hammer ; otherwise, you might only find nails.

Like any other tool, a design pattern’s usefulness depends on context. Each one provides a benefit in certain situations and also comes with its share of drawbacks. Every decision in software development comes with compromises.

Are you generating a lot of GameObjects on the fly? Does it impact your performance? Can restructuring your code fix that? Be aware of these design patterns, and when the time is right, pull them from your gamedev bag of tricks to solve the problem at hand.

In addition to the Gang of Four’s Design Patterns, Game Programming Patterns by Robert Nystrom is another standout resource, currently available for free as a web-based edition. The author details a variety of software patterns in a no-nonsense manner.

In our new e-book, you can dive into the sections that explain common design patterns, such as factory, object pool, singleton, command, state, and observer patterns, plus the Model View Presenter (MVP), among others. Each section explains the pattern along with its pros and cons, and provides an example of how to implement it in Unity so you can optimize its usage in your project.

Unity already implements several established gamedev patterns, saving you the trouble of writing them yourself. These include:

• Game loop : At the core of all games is an infinite loop that must function independently of clock speed, since the hardware that powers a game application can vary greatly. To account for computers of different speeds, game developers often need to use a fixed timestep (with a set frames-per-second) and a variable timestep where the engine measures how much time has passed since the previous frame. Unity takes care of this, so you don’t have to implement it yourself. You only need to manage gameplay using MonoBehaviour methods like Update, LateUpdate, and FixedUpdate.
• Update : In your game application, you’ll often update each object’s behavior one frame at a time. While you can manually recreate this in Unity, the MonoBehaviour class does this automatically. Use the appropriate Update, LateUpdate, or FixedUpdate methods to modify your GameObjects and components to one tick of the game clock.
• Prototype : Often you need to copy objects without affecting the original. This creational pattern solves the problem of duplicating and cloning an object to make other objects similar to itself. This way you avoid defining a separate class to spawn every type of object in your game. Unity’s Prefab system implements a form of prototyping for GameObjects. This allows you to duplicate a template object complete with its components. Override specific properties to create Prefab Variants or nest Prefabs inside other Prefabs to create hierarchies. Use a special Prefab editing mode to edit Prefabs in isolation or in context.
• Component :Most people working in Unity know this pattern. Instead of creating large classes with multiple responsibilities, build smaller components that each do one thing. If you use composition to pick and choose components, you can combine them for complex behavior. Add Rigidbody and Collider components for physics, or a MeshFilter and MeshRenderer for 3D geometry. Each GameObject is only as rich and unique as its collection of components.

Both the e-book and a sample project on the use of design patterns are available now to download for free. Review the examples and decide which design pattern best suits your project. As you gain experience with them, you’ll recognize how and when they can enhance your development process. As always, we encourage you to visit the forum thread and let us know what you think of the e-book and sample.

More From Forbes

How the way people learn to code is changing (for the better).

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Tigran Sloyan is the cofounder and CEO of CodeSignal , a technical interview and assessment platform.

More people than ever are learning to code today. With the current digital transformation across sectors, companies in just about every industry need software developers and engineers—and the skills they need are constantly evolving .

Recent innovations in cloud computing, mobile development and artificial intelligence (AI) have created demand for entirely new technical skill sets. For example, while few companies integrated AI into their technology or business operations several years ago, over 35% now use AI —and another 42% are exploring its possibilities.

The problem is that the way individuals learn technical skills hasn’t kept pace with technological innovation. The content of traditional computer science (CS) programs is years behind current technology, and learning from lectures or videos alone is often not as effective as gaining hands-on experience. Practical application is crucial for mastering technical skills, as it allows learners to engage directly with the technology and solve real-world problems.

Today, innovations in AI open up a new world of possibilities for how online resources can help individuals practice and master the most in-demand technical skills.

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Best 5% interest savings accounts of 2024, ai is improving how we learn to code..

There are at least three ways that AI is already revolutionizing how people learn to code:

1. AI makes one-on-one tutoring scalable and accessible.

Decades of educational research show that one-on-one tutoring produces the best educational outcomes . With human teachers, a one-to-one student-to-teacher ratio is expensive and impossible to scale. Today, however, AI-powered tutors can provide high-quality personalized feedback and learning support—in a way that is infinitely scalable and affordable for anyone learning to code. This allows anyone, anywhere, to access the type of one-on-one support that best facilitates learning.

2. AI allows for learning that is personalized to an unprecedented degree.

Traditional CS courses and even online courses use a one-size-fits-all model, where all students follow the same curriculum and complete the same assignments. For some students, this curriculum will progress too quickly, leaving them overwhelmed and frustrated; for others, the curriculum will be painfully slow and unengaging.

Today’s AI-powered learning platforms can meet students where they are to keep them learning in a state of flow by tailoring their lessons and practices to provide an appropriate level of challenge.

3. AI uniquely facilitates mastery-based learning.

With the ability to assess each student’s level of skill proficiency based on the code they’ve written, AI-powered learning platforms can move students along in the curriculum only when they’ve achieved the proficiency needed to advance. If a student is stuck, the platform can offer personalized support and practice opportunities to help them master each skill.

This is a major advancement from once-size-fits-all courses and coding boot camps, where all students are expected to follow the same course progression, regardless of their level of proficiency.

AI will transform learning.

Providing one-on-one support, personalizing learning experiences and helping students achieve skill mastery are just three of the ways AI is already revolutionizing how individuals learn to code. As AI technology continues to advance, it will likely transform learning in ways that we cannot yet even imagine. And beyond the technical innovations, AI can potentially democratize access to learning to code and, in turn, to building a career in tech.

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