difference between programming and problem solving

What Is The Difference Between Coding And Programming

• Technology & Innovation
• Emerging Tech

Introduction

When it comes to the world of technology and software development, two terms that are often used interchangeably are “coding” and “programming.” While they both involve writing instructions for computers to follow, there are significant differences between the two. Understanding the distinction can help aspiring developers choose the right path and clarify misconceptions.

Coding refers to the process of translating human instructions into a language that computers can understand. It involves writing lines of code using specific programming languages such as HTML, CSS, or JavaScript. Coders focus on creating the structure, layout, and design of a website or application, ensuring that it functions as intended. They mainly work on the frontend, dealing with the visual elements and user interface.

Programming , on the other hand, encompasses a broader scope. It involves not only writing code but also developing algorithms, analyzing problems, and designing solutions. Programmers use coding as a tool to implement their ideas but also have a deep understanding of the underlying concepts and principles. They work on both frontend and backend development, dealing with databases, server-side logic, and complex functionalities.

The relationship between coding and programming is similar to that of words and sentences. Coding is like putting together individual words to convey a specific meaning, while programming is like composing those words into structured sentences to express a comprehensive thought. Coding is a part of programming, but programming goes beyond just coding.

Now that we have a basic understanding of what coding and programming mean, let’s explore the key differences between the two.

Definition of Coding

Coding, in simple terms, is the process of creating instructions for computers using programming languages. It involves writing lines of code that specify the desired actions and behaviors of a software application or a website. Coders use specific programming languages such as HTML, CSS, or JavaScript to communicate with computers and tell them what to do.

At its core, coding is about translating human instructions into a language that computers can understand. It requires attention to detail and precision to ensure that the code is written correctly and accurately. Coders need to understand the syntax, rules, and conventions of the programming language they are using to effectively communicate with computers.

Coding is primarily focused on the visual elements and user interface of a software application. Coders are responsible for creating the structure, layout, and design of websites or applications. They use HTML for building the structure, CSS for styling and formatting, and JavaScript for adding interactive features and functionality.

In addition to writing code, coders also need to understand how to use various tools and frameworks that enhance their coding process. These tools can help with editing and debugging code, managing version control, and collaborating with other developers. Examples of popular coding tools include text editors like Sublime Text or Visual Studio Code, version control systems like Git, and integrated development environments (IDEs) like IntelliJ IDEA or Eclipse.

Coding is an essential skill for anyone interested in web development or building software applications. It allows developers to bring their ideas to life by creating functional and visually appealing websites or applications. While coding may seem daunting at first, with practice and continuous learning, anyone can become proficient in writing code and mastering the art of coding.

In the next section, we will delve into the definition of programming and explore the relationship between coding and programming.

Definition of Programming

Programming is a broader concept that encompasses more than just writing code. It involves the process of developing algorithms, analyzing problems, and designing solutions using coding as a tool. Programmers use their knowledge of coding and programming principles to create complex software applications that perform specific tasks and solve real-world problems.

Unlike coding, which focuses primarily on the frontend and user interface, programming involves working on both the frontend and backend aspects of software development. Programmers deal with databases, server-side logic, and complex functionalities to ensure that the software application functions as intended.

One of the key aspects of programming is the ability to think logically and analytically. Programmers need to break down complex problems into smaller, manageable parts and design algorithms to solve them. They also need to consider factors such as efficiency, scalability, and security when developing software applications.

A programmer’s role goes beyond simply writing code. They need to be proficient in various programming languages and have a deep understanding of data structures, algorithms, and software design principles. Additionally, programmers often collaborate with other team members, such as software architects and project managers, to ensure that the software application meets the desired requirements and specifications.

Programming also involves using different methodologies and paradigms to approach software development. This includes object-oriented programming (OOP), where code is organized around objects and their interactions, and functional programming , which focuses on using pure functions and immutable data.

To enhance their programming skills, developers often rely on software development frameworks and tools. These tools provide pre-built components, libraries, and functionality that can be used to streamline the development process and improve productivity. Popular programming frameworks include React, Angular, and Django, while tools like debugging environments and integrated development environments (IDEs) offer features such as code completion, syntax highlighting, and code analysis.

In the next section, we will explore the relationship between coding and programming, highlighting their key differences and similarities.

The Relationship between Coding and Programming

Coding and programming are closely related but distinct concepts within the world of software development. Coding is a subset of programming, as it involves writing the specific instructions in a programming language to create the desired output. Programming, on the other hand, encompasses the larger process of designing, organizing, and implementing the code based on logical and problem-solving principles.

Think of coding as the technical aspect of programming. It focuses on the syntax and structure of the code, ensuring that it follows the rules and conventions of the chosen programming language. Coders are responsible for writing clean and efficient code to achieve the desired outcomes. Their main focus is on the frontend, dealing with the visual elements and user interactions.

Programming, on the other hand, takes a more holistic approach. Programmers analyze the problem at hand, design the solution, and then implement it using coding as a tool. They consider factors such as performance, scalability, and maintainability when creating software applications. Programmers work on both frontend and backend aspects, handling databases, server-side logic, and complex functionalities.

While coding is a specific task within the larger scope of programming, it is an essential skill for programmers. Without coding expertise, programmers would not be able to bring their ideas to life and create functional software solutions. Coding provides the means to implement the logical design and algorithms devised by programmers.

In many cases, programmers also need to understand and work with existing codebases. This could involve debugging, refactoring, and adding new features or functionalities to an existing codebase. This requires a combination of coding skills to understand and modify the code correctly, as well as programming skills to have a broader understanding of how the changes fit into the overall software architecture.

Ultimately, coding and programming go hand in hand. Coding is a tool used by programmers to translate their ideas into functional software applications. Both skills are essential for anyone pursuing a career in software development, and having a solid foundation in coding is crucial to becoming an effective programmer.

Now that we have explored the relationship between coding and programming, let’s dive into the key differences between the two in the next section.

Coding vs Programming: Key Differences

While coding and programming are closely related, there are several key differences between the two. Understanding these differences can help clarify their roles and responsibilities within the software development process. Let’s explore some of the key distinctions:

Scope: Coding focuses mainly on the implementation and execution of specific tasks using programming languages. It deals with the frontend aspects such as user interfaces and visual elements. Programming, on the other hand, encompasses a broader scope, including analyzing problems, designing solutions, and implementing them using coding as a tool. Programmers work on both frontend and backend aspects, addressing complex functionalities and database management.

Abstraction: Coding is often more concrete and operational, involving writing specific lines of code to achieve desired outcomes. It deals with step-by-step instructions and direct interactions with the computer. Programming, on the other hand, involves more abstract thinking. Programmers create algorithms, design software architecture, and think in a logical and systematic manner to solve problems. They focus on concepts like data structures, algorithms, and design patterns.

Problem-Solving: While coding involves solving specific technical challenges related to implementing features and functionalities, programming goes beyond that. Programmers analyze problems holistically and identify the most efficient and effective ways to solve them. They consider various factors such as performance, scalability, and user experience when designing software solutions.

Knowledge and Skills: Coding requires a deep understanding of specific programming languages, their syntax, and rules. Coders need to be familiar with HTML, CSS, JavaScript, or other relevant languages. They must also be comfortable using coding tools and frameworks. Programmers, on the other hand, need a broader skill set. They must have a deep understanding of coding principles but also possess knowledge in areas such as data structures, algorithms, software design patterns, and problem-solving techniques.

Responsibilities: Coders primarily focus on writing code and ensuring that it functions as intended. They are responsible for implementing the frontend elements and ensuring the user interface is visually appealing and user-friendly. Programmers, on the other hand, have a more comprehensive role. They analyze problems, design software solutions, coordinate with team members, and oversee the entire development process.

In summary, while coding and programming are interrelated, they have distinct differences in scope, level of abstraction, problem-solving approach, and responsibilities. Coding is a subset of programming, focusing on the implementation of specific tasks using programming languages, while programming encompasses a broader set of skills and responsibilities that involve problem-solving, design, and implementation.

Now, let’s find out the similarities between coding and programming in the next section.

Coding vs Programming: Similarities

While coding and programming have their distinct differences, there are also several similarities between the two. Let’s explore these shared aspects:

Language Dependence: Both coding and programming rely on specific programming languages to communicate with computers. Whether it’s HTML, CSS, JavaScript, Python, or any other language, both coding and programming require a deep understanding of the syntax, rules, and conventions of the chosen language.

Logic and Problem-Solving: Both coding and programming require logical thinking and problem-solving skills. Whether it’s writing a line of code to execute a specific functionality or designing an algorithm to solve a complex problem, both coding and programming involve breaking down problems into smaller, manageable parts and finding creative solutions.

Attention to Detail: Both coding and programming demand attention to detail. Writing clean and error-free code is essential in both cases. Whether it’s ensuring proper indentation, correct syntax, or following best practices, both coders and programmers must pay close attention to the specifics of their code.

Collaboration: Both coding and programming often involve collaboration with other team members. Whether it’s designers, project managers, or other developers, both coders and programmers need to communicate effectively and work together to create successful software solutions.

Continuous Learning: Both coding and programming require a commitment to continuous learning. Technology and programming languages are constantly evolving, and staying up-to-date with the latest advancements is crucial for both coders and programmers. They both need to keep learning, experimenting, and adapting their skills to stay relevant in the ever-changing field of software development.

Creativity: Both coding and programming offer opportunities for creativity. Whether it’s designing an elegant user interface, implementing innovative features, or designing efficient algorithms, both coders and programmers can express their creativity while solving problems and building software solutions.

In summary, while coding and programming have their distinct differences, they also share several similarities. Both require a deep understanding of programming languages, logical thinking, attention to detail, collaboration, continuous learning, and a touch of creativity.

Understanding the similarities and differences between coding and programming can help aspiring developers choose the right path and navigate the field of software development effectively.

Now, let’s explore when it is appropriate to use coding and programming in the next section.

When to Use Coding and Programming

Knowing when to use coding and programming is essential to effectively approach software development projects. While coding and programming are closely related, their application varies based on the complexity, scope, and requirements of the project. Let’s explore when it is appropriate to use coding and programming:

Using Coding: Coding is primarily used when working on the frontend aspects of a software application or website. If the project requires designing the user interface, implementing visual elements, and ensuring a smooth user experience, coding is the go-to approach. Coders use specific programming languages like HTML, CSS, and JavaScript to bring the design to life and create interactive and visually appealing user interfaces.

Using Programming: Programming is generally applied when dealing with more complex projects that require a holistic approach to problem-solving and software development. If the project involves creating complex functionalities, managing databases, implementing algorithms, and ensuring high performance and scalability, programming is essential. Programmers design the software architecture, develop efficient algorithms, handle backend logic, and make strategic decisions to ensure the software meets the desired requirements.

The decision to use coding or programming depends on the specific requirements of the project and the desired outcomes. Smaller projects that focus primarily on the frontend and require a quick implementation of specific features may rely more on coding. On the other hand, larger projects that demand a comprehensive approach, considering factors like scalability, performance, and robustness, require programming.

It is worth noting that coding and programming often go hand in hand. A project may involve both coding and programming aspects, especially in larger-scale applications. In such cases, coders and programmers collaborate closely to ensure a seamless integration of frontend and backend functionalities.

Additionally, as projects evolve and scale, the need for programming may increase. While coding can handle the initial implementation, programming allows for more flexibility, scalability, and complexity management as the project grows.

Ultimately, the choice between coding and programming depends on the project requirements, the desired functionalities, and the level of complexity involved. Understanding the project’s scope and goals is vital in determining when and how to leverage coding and programming effectively.

Now, let’s wrap up the discussion and summarize the key points we have covered so far.

In conclusion, coding and programming are closely related yet distinct concepts within the field of software development. Coding refers to the process of writing instructions in a programming language to create a functional design and user interface. Meanwhile, programming encompasses a broader scope, involving problem-solving, algorithm design, and the implementation of complex functionalities.

While coding and programming have their differences, they also share similarities. Both require a deep understanding of programming languages, logical thinking, attention to detail, collaboration, continuous learning, and a touch of creativity. Both coding and programming are necessary skills for aspiring developers, with coding serving as an essential component of programming.

Knowing when to use coding and programming is essential in approaching software development projects effectively. Coding is primarily used for frontend development, focusing on user interfaces and visual elements. On the other hand, programming is applied in more complex projects that involve backend development, algorithm design, and scalability considerations.

It’s important to note that the line between coding and programming can be flexible and often blurs in real-world projects. Smaller-scale projects may rely more on coding, while larger-scale projects demand a more comprehensive programming approach to handle complexities efficiently.

To succeed in the field of software development, individuals should strive to understand both coding and programming concepts. Building a strong foundation in coding fundamentals is crucial for becoming an effective programmer, as it provides the necessary skills to translate ideas into functional software solutions.

Whether one’s goal is to be a proficient coder or a skilled programmer, continuous learning, practice, and staying up to date with the latest trends and technologies are key. The field of software development is constantly evolving, so developers must adapt their skills and knowledge to remain competitive.

Overall, coding and programming are essential components of software development. While they have their distinctions, they work together harmoniously to create innovative and functional software solutions that drive technological advancements and improve our digital experiences.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Crowdfunding
• Cryptocurrency
• Digital Banking
• Digital Payments
• Investments
• Console Gaming
• Mobile Gaming
• VR/AR Gaming
• Gadget Usage
• Gaming Tips
• Online Safety
• Software Tutorials
• Tech Setup & Troubleshooting
• Buyer’s Guides
• Comparative Analysis
• Gadget Reviews
• Service Reviews
• Software Reviews
• Mobile Devices
• PCs & Laptops
• Smart Home Gadgets
• Content Creation Tools
• Digital Photography
• Video & Music Streaming
• Online Security
• Online Services
• Web Hosting
• WiFi & Ethernet
• Browsers & Extensions
• Communication Platforms
• Operating Systems
• Productivity Tools
• AI & Machine Learning
• Cybersecurity
• IoT & Smart Devices
• Virtual & Augmented Reality
• Latest News
• AI Developments
• Fintech Updates
• Gaming News
• New Product Launches

Sanjuksha Nirgude Soaring High with Robotics

Openstack backup and recovery software, related post, apple wallet availability in the pocketguard app: sync your apple card, apple cash, and savings from apple card accounts, ai writing: how it’s changing the way we create content, how to find the best midjourney alternative in 2024: a guide to ai anime generators, unleashing young geniuses: how lingokids makes learning a blast, 10 best ai math solvers for instant homework solutions, 10 best ai homework helper tools to get instant homework help, related posts.

How To Program Spectrum Remote To Smart TV?

How Long Is A Coding Bootcamp

Guide on Coding vs Programming : What’s The Difference

How Long Is A PHP Program

How To Program A Universal Remote To RCA DVD Surround Sound System

What Is Sequence In Coding

Where To Practice Coding

What Is A Variable In Coding

Recent stories.

5 Ways to Improve IT Automation

What is Building Information Modelling?

How to Use Email Blasts Marketing To Take Control of Your Market

Learn To Convert Scanned Documents Into Editable Text With OCR

Top Mini Split Air Conditioner For Summer

• Privacy Overview
• Strictly Necessary Cookies

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

How to think like a programmer — lessons in problem solving

By Richard Reis

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

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

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

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

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

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

Why is this important?

Problem solving is the meta-skill.

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

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

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

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

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

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

Have a framework

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

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

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

Soon, you too will know them.

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

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

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

Here are the steps:

1. Understand

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

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

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

Most programmers know this feeling.

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

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

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

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

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

To get a good plan, answer this question:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How to practice? There are options out the wazoo!

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

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

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

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

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

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

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

Like I said, all problems share similar patterns.

That’s all folks!

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

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

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

Phew… Pretty cool right?

Finally, I wish you encounter many problems.

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

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

Now, go solve some problems!

And best of luck ?

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

Thanks for reading! If you enjoyed it, test how many times can you hit in 5 seconds. It’s great cardio for your fingers AND will help other people see the story.

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

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

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

Computational thinking & problem-solving

Wing (2006, 2011) defined computational thinking as the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by a computer. [2]

Computational Thinking (CT) is a process that generalizes a solution to open-ended problems. Open-ended problems encourage full, meaningful answers based on multiple variables, which require using decomposition , data representation, generalization, modeling, and algorithms found in Computational Thinking. Computational Thinking requires the decomposition of the entire decision making process, the variables involved, and all possible solutions, ensuring that the right decision is made based on the corresponding parameters and limitations of the problem. The term computational thinking was first used by Seymour Papert in 1980 and again in 1996. Computational thinking can be used to algorithmically solve complicated problems of scale, and is often used to realize large improvements in efficiency [3]

• 1.1 Thinking procedurally
• 1.2 Decisions
• 1.3 Thinking logically
• 1.4 Thinking ahead
• 1.5 Thinking concurrently
• 1.6 Thinking abstractly
• 1.7 Connecting computational thinking and program design
• 1.8 Use of programming languages
• 2 Standards
• 3 References

The big ideas in computational thinking [ edit ]

Thinking procedurally [ edit ].

• Procedural thinking
• Evaluating process
• Sub-process

Decisions [ edit ]

Thinking logically [ edit ].

• Logical rules

Thinking ahead [ edit ]

• Inputs and outputs
• Pre-conditions

Thinking concurrently [ edit ]

• Concurrency

Thinking abstractly [ edit ]

• Abstraction

Connecting computational thinking and program design [ edit ]

• Linear arrays
• Applied algorithmic thinking

Use of programming languages [ edit ]

• Conditionals
• Collections

Standards [ edit ]

These standards are used from the IB Computer Science Subject Guide [4]

References [ edit ]

• ↑ http://www.flaticon.com/
• ↑ http://pact.sri.com/downloads/Assessment-Design-Patterns-for-Computational%20Thinking-Practices-Secondary-Computer-Science.pdf
• ↑ https://en.wikipedia.org/wiki/Computational_thinking
• ↑ IB Diploma Programme Computer science guide (first examinations 2014). Cardiff, Wales, United Kingdom: International Baccalaureate Organization. January 2012.

Separate into simpler constituents.

Produce a plan, simulation or model.

Apply knowledge or rules to put theory into practice.

Provide an answer from a number of possibilities. Recognize and state briefly a distinguishing fact or feature.

Assess the implications and limitations; make judgments about the ideas, works, solutions or methods in relation to selected criteria.

Give a detailed account including reasons or causes.

Reach a conclusion from the information given.

Give a brief account.

anomalous or exceptional conditions requiring special processing – often changing the normal flow of program execution

Give a detailed account or picture of a situation, event, pattern or process.

Develop information in a diagrammatic or logical form.

Make clear the differences between two or more concepts or items.

Offer a considered and balanced review that includes a range of arguments, factors or hypotheses. Opinions or conclusions should be presented clearly and supported by appropriate evidence.

Break down in order to bring out the essential elements or structure. To identify parts and relationships, and to interpret information to reach conclusions.

Propose a solution, hypothesis or other possible answer.

The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed.

Obtain the only possible answer.

A unit of abstract mathematical system subject to the laws of arithmetic.

Give a specific name, value or other brief answer without explanation or calculation.

Give the precise meaning of a word, phrase, concept or physical quantity.

• Computational thinking
• Programming
• Problem-solving
• Essential idea

• Streaming and Technology

What Is Problem-Solving Programming?

Are you interested in a career in computer programming? Are you wondering if you have what it takes? Perhaps you’re fluent in relevant programming languages, but you wonder what other skills are essential to a successful programming career.

Here, we’ll look at how problem-solving skills are essential for programmers. This article will equip you to leverage your problem-solving skills on your programming resume.  

A Career in Programming

What is programming? We’re not talking about television programming here. Programmers are also known as computer programmers, systems programmers, software engineers, or coders. Programmers “ are tasked with designing and creating software programs, integrating systems and software, training end-users, analyzing algorithms, modifying source-code, writing system instructions, debugging, and maintaining operating systems.” Fortunately, there are solutions to address these issues and one of them is Sentry. Sentry is an error monitoring tool designed to identify and diagnose bugs within your software. Moreover, Sentry is not your only option. There are numerous  alternatives to Sentry  available in the market, providing software engineers with a diverse set of tools to monitor and promptly address crashes in real-time.

What Is Problem-Solving in Programming?

Have you ever encountered a “computer bug?” Maybe you were playing a video game, using an app, or completing work or school assignments on your computer. The bug or glitch became evident when the computer crashed, the screen froze, or the program did not behave as you expected.  

Computer programmers use problem-solving skills, along with an in-depth knowledge of coding, to find and fix issues like these. Each time, the programmer will practice some or all of the following steps .

• Identify the problem. Before a computer program, app, or game is released to the public, it is scrutinized by its makers. Programming teams use the software or play the game, searching for errors. They may recruit others through beta testing programs to use the application and report any problems.
• Understand the problem. Once an error is reported, the programmer must evaluate the issue to determine possible causes and solutions. It’s important to take your time with this step – make sure you really understand every aspect of the problem and your not making assumptions based on past experience.
• Work through the problem. To solve the problem, you’ll need to see it for yourself. Use the application and witness the issue. Then, work through it several more times with different variables to see if the issue is consistent.
• Pseudocode. Write out what you need the lines of code to do before translating it to actual code.
• Translate. Turn the pseudocode into code.
• Test your code. Run the program and find out if it functions as it is supposed to.
• Debug. Fix any errors as you go along.
• Simplify. Errors can arise when code is more complicated than it has to be.
• Take notes. Likely, you won’t be the only one to ever work with your lines of code. Even if you are, you may find that in a few months, you don’t remember exactly what each and every line accomplished. So, take notes on what each line is for.
• Ask for feedback. Just as testing may have revealed the problem in the first place, so it can aid you in identifying any additional issues. Other coders, developers, or programmers may see solutions you don’t. They can make suggestions that will improve the product or process overall.

Improve Your Problem-Solving Skills

Many universities offer online courses that can help you learn creative problem-solving skills that relate directly to information technology applications.

You can also practice problem-solving techniques in everyday situations. When confronted with a challenge, try the following: 

• Identify the problem.
• Get the facts – research similar problems and ask questions. Practice active listening.
• Find possible solutions – brainstorm. Make backup plans in case solutions don’t work as planned.
• Decide on a plan. Weigh the pros and cons of each possible solution, and choose the one that is best overall. If multiple people are involved in the decision-making process, try to arrive at a decision that meets everyone’s needs.
• Act! Once you’ve decided, implement your plan.
• Look for results. Make observations and talk to others involved. Is your plan achieving the desired results? If not, find more facts and follow up with other possible solutions.

Even problem-solving skills from your daily life will serve you well when challenges arise at work.

How to List Problem-Solving Skills on Your Resume

You can include problem-solving skills on your resume in several ways:

• In your skills list
• In your skills summary or core competencies
• Within your job descriptions

You don’t just have to use the term “problem-solving,” either. Show rather than tell. Could you include statements similar to the following that match your unique qualifications?

• “Assisted a team of 6 in debugging a flagship program two days before the product launch.”
• “Identified 32 discrete lines of code that were in error; debugged, simplified, and optimized the code in question.”
• “Communicated with 24 beta testers from around the world over the course of 5 weeks to ready the program for launch.”

You may also use other terminology related to problem-solving, including creativity, analysis, designing, critical thinking, drawing conclusions, focus, experimenting, evaluation, communication, decision-making, and assessment.

{{ activeMenu.name }}

• Python Courses
• JavaScript Courses
• Artificial Intelligence Courses
• Data Science Courses
• React Courses
• Ethical Hacking Courses
• View All Courses

Fresh Articles

• Python Projects
• JavaScript Projects
• Java Projects
• HTML Projects
• C++ Projects
• PHP Projects
• View All Projects

• Python Certifications
• JavaScript Certifications
• Linux Certifications
• Data Science Certifications
• Data Analytics Certifications
• Cybersecurity Certifications
• View All Certifications

• IDEs & Editors
• Web Development
• Frameworks & Libraries
• View All Programming
• View All Development

• App Development
• Game Development
• Courses, Books, & Certifications
• Data Science
• Data Analytics
• Artificial Intelligence (AI)
• Machine Learning (ML)
• View All Data, Analysis, & AI

• Networking & Security
• Cloud, DevOps, & Systems
• Recommendations
• Crypto, Web3, & Blockchain
• User-Submitted Tutorials
• View All Blog Content

• Python Online Compiler
• JavaScript Online Compiler
• HTML & CSS Online Compiler
• Certifications
• Programming
• Development
• Data, Analysis, & AI
• Online Python Compiler
• Online JavaScript Compiler
• Online HTML Compiler

Don't have an account? Sign up

Forgot your password?

Already have an account? Login

Have you read our submission guidelines?

Go back to Sign In

What Is Programming? | Ultimate Guide To Programming

Have you heard the term computer programming and found yourself asking questions like ‘what does programming mean’ and ‘what is programming used for’?

In this article, we’ll provide the ultimate guide to explain programming, helping you to answer the question, what is programming?

We’ll also cover the difference between computer programming and coding, explain what a programming language is, the benefits of programming, and some helpful tips to help you get started if you’re a beginner. 

• What Is Programming?

Let’s start with a simple programming definition. Programming involves instructing computers to perform tasks. It could be a simple task like displaying the sum of two numbers or solving complex problems like the operation of self-driving cars.

Having defined programming, let’s consider where it fits in the modern world. Well, it’s safe to say that programming intertwines with our everyday lives in more ways than we probably even know.

The use of programming to make life more convenient can be seen in almost all walks of life, from business, entertainment, transportation, health care, agriculture, sports, politics, and so on.

The First Computer Programmer

19th-century English mathematician Ada Lovelace has been credited as the first computer program creator with an algorithm to calculate Bernoulli numbers.

Together with Charles Babbage, they developed what’s regarded as the first computer. It was a primitive mechanical device called the Analytical Engine, and Lovelace wrote her algorithm for this device.

Since then, programming has evolved to become a far-reaching field within the tech industry and beyond.

• What Are Programming Languages?

You may be wondering how programmers (humans) pass instructions to computers. This is done with programming languages. 

Human languages like English and Spanish have a syntax to guide the composition of phrases and sentences. Programming languages also have a syntax that guides how programmers compose the logic behind the code written.

When we refer to programming languages, we can usually classify them into four distinct categories.

1. Machine Language

Ever heard the phrase, ‘ computers understand only 1s and 0s ’? This is machine language (or machine code). It’s a set of binary digits (1s and 0s) used by a computer’s CPU to execute directly. 

A computer has transistors, tiny electric switches with two options, on (for 1s) or off (for 0s). The computer’s CPU can read combinations of these on/off electric signals and create the desired output. In general, machine code is referred to as a low-level language.

And just for fun, here’s an example of machine code for the word ‘Hello’ .

Machine Code Example:

2. Assembly Language (ASM)

These were introduced because machine code can be tedious and prone to error. So, instead of using binary digits, code is written with commands that include symbols and characters. 

As a computer can only execute 0s and 1s (machine code), programmers must use an assembler to translate assembly language into machine code before execution.

Still, this isn’t human-friendly, and it requires in-depth knowledge of computer memory and CPU processes to make sense of it. Like machine code, assembly language can also be referred to as a low-level language.

We’ve also included an example of assembly language to print the word ’Hello’ to a screen using x86 assembly language .

Assembly Language Example:

3. Mid-Level Language

When you move away from low-level languages, you tend to encounter the higher-level languages that most people think of when they hear the term programming language .

And while it’s okay to bundle these all together, it can help to divide these modern languages into two sub-categories, starting with mid-level languages.

These allow you to write in more human-friendly syntax while retaining access to a computer system's abstraction layer . You can still control the underlying hardware directly, making mid-level languages a bridge between lower and higher-level languages.

The Complete Python Bootcamp From Zero to Hero in Python

Mid-level languages use compilers to translate their syntax into machine code, allowing the computers to execute the code. 

Some common examples of mid-level languages include C, Pascal, and Cobol. And to keep up our habit of looking at actual code, we’ve added a small block of C code to output the word ‘Hello’ to the screen.

Mid-Level Language Example:

4. High-Level Language

Much like mid-level languages, when you hear the term programming language , these are often what’s being referred to. Most computer programmers use them for their day-to-day activities and work. 

High-level languages use special translators called compilers to translate into machine code, allowing computers to execute the code logic. 

These languages have a human-friendly syntax and include general purpose programming languages like Python, JavaScript, and PHP. You can also count domain-specific languages like SQL and HTML as high-level.

We’ve included a JavaScript code snippet that outputs the word ‘Hello’ to the console screen.

High-Level Language Example:

Trying to choose a programming language? Check out our article on the Best Programming Languages to Learn

• What’s the Difference Between Coding & Programming?

You’ll often hear the terms ‘coding’ and ‘programming’ being used interchangeably, but some differences are worth noting.

• Coding: A core element of programming that involves translating the logic of a program so that computers can understand and execute it. This covers the writing of the lines of code that make up the program, so it is more concerned with using the syntax of programming languages to communicate with computers.
• Programming: This encompasses more than just writing code. It usually includes other aspects of software creation, like identifying program requirements, translating requirements into code, debugging, testing, creating documentation, and more.

The relationship between coding and programming can be best described by considering what’s involved in writing a novel.

Coding is akin to writing the sentences and paragraphs that form the novel, while programming involves creating broader elements, like plot, setting, character development, narrative, etc.

• What Are the Benefits of Programming?

You may be thinking, okay, this is cool, but why should I bother? Technology is everywhere, and the benefit of computer programming cannot be overemphasized.

For example, you are reading this article because your device is programmed to display it to you when you implement the correct clicks. Let’s see some reasons why you should care about programming.

• Work faster: Computers are amazingly fast, and with programming, we can utilize their power to solve complex problems that would take us forever. An example is the power of instant messaging applications.
• Convenience: Most of the convenient ways of doing things in our modern world are due to programming. For example, due to programming, you can be in the comfort of your home, order some food and have it delivered in minutes.
• Work 24/7: Computers can work 24/7 without getting exhausted or bored! So you can write programs for computers to handle mundane and repetitive tasks, giving you free time to spend on tasks that require creative thinking. 
• Career opportunities: There are many career prospects when you learn how to program. Most businesses have websites and need programmers to create and maintain those websites. Other prospects include mobile app development, game development, data science, artificial intelligence, and more.
• Logical & problem-solving skills: At its core, programming is problem-solving. Creating computer programs challenges you to think through problems, consider solutions, and find the most efficient one,  helping you improve your analytical skills. So whether you’re using structured programming, object-oriented programming, or another approach, you’ll be solving problems.
• Have fun building cool stuff: You can create your own games or applications! Imagine creating your own app, putting it on the AppStore or Play Store, and getting thousands of downloads! Sounds exciting, right? It’s possible when you learn to program.

Ready to build cool stuff? Check out the Best Programming Languages for Games

• How to Learn Programming

If you’re interested in learning programming, here’s some advice and steps you can take to ensure a smooth learning journey.

• Figure out your reasons: Decide why you want to learn to program. You may want to switch careers to game development or software development, build a passion project, or learn as a hobby. Your reason for learning will help you choose the best way to set about this exciting journey.
• Choose a programming language: Many beginners make the mistake of learning multiple languages at once, but it’s better to pick one to start and learn another later.
• Choose a learning resource: Learning options include taking an online course , reading a book , joining a boot camp, or a combination of any of these. Identify your preferred style of learning and choose the best option for you.
• Build projects: As soon as possible, start building projects! This will help cement the concepts you’ll learn through an online course, book, or tutorial. Build stuff, break it along the way, and figure out how to fix things. It’s fun!
• Join a community: Learning how to code can be hard. But when you join a community of like-minded learners, it can be less intimidating. Make the most of help from programmers with more experience than you.

If you’ve read this far, you have learned the answer to the question, what is programming? 

And with programming explained, we could discuss the difference between coding and programming before covering the different types of programming languages, including lower-level (machine code &  assembly language) and higher-level (mid-level & high-level) languages. 

We also delved into the many benefits of programming, including some tips and advice for you to follow if you’re ready to learn to program.

Undoubtedly, computer programming is a huge topic, and there is a lot to learn and discover! Hopefully, this article has piqued your interest while helping you learn what is programming in simple words. 

And, of course, we hope you’re now feeling ready and excited to explore more.

Excited to start programming? Check out our article on

• Frequently Asked Questions

1. What Is the Main Purpose of Programming?

It is to maximize the power of computers. Programming allows us to solve problems with computers quickly, cheaply, and efficiently.

2. What Software Is Used for Programming?

Programming requires us to use various software pieces, some of the most essential being an IDE (Integrated Development Environment) or code editors like Visual Studio Code or Atom and a version control system like Git.

3. What Is the Difference Between Coding and Programming? 

Coding is when we use the syntax of a programming language to write computer programs. Programming entails additional processes like identifying program requirements, debugging, testing, etc. 

People are also reading:

• What are Programming Languages?
• Best Programming Books
• Best Programming Interview Questions
• Best Programming Languages to learn
• Programming Languages for Getting a Jobs
• What is Functional Programming ?
• What is Procedural Programming?
• Programming Paradigm
• How to learn to program?
• Free Coding Bootcamp
• Best Web Development IDE
• How to Code a Game?

Entrepreneur, Coder, Speed-cuber, Blogger, fan of Air crash investigation! Aman Goel is a Computer Science Graduate from IIT Bombay. Fascinated by the world of technology he went on to build his own start-up - AllinCall Research and Solutions to build the next generation of Artificial Intelligence, Machine Learning and Natural Language Processing based solutions to power businesses.

Subscribe to our Newsletter for Articles, News, & Jobs.

Disclosure: Hackr.io is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.

In this article

• GitHub Copilot vs Amazon CodeWhisperer | Who's Best in 2024? Artificial Intelligence (AI) Code Editors IDEs AI Tools
• 10 Python Concepts I Wish I Knew Earlier For Interviews [2024] Python Career Development Interview Questions
• 7 Best AI Coding Assistants In 2024 [Free + Paid] Artificial Intelligence (AI) Code Editors IDEs AI Tools

Please login to leave comments

I know computers but not aware of programming. What is programming in computer?

5 years ago

Where can I learn basic computer programming?

Where to get started with programming for beginners?

Can you define programming?

This might be a useful article: https://medium.com/@i.oleks/how-to-apply-for-google-summer-of-code-95c1bfcd41a5

4 years ago

it depends on what is your goal .If your goal is to do a job in a specific company as a python developer(a specific field like web dev , app dev , game dev etc) .Then search that company and read their requirements for a job .then according to their requirements complete open source projects and share them with other peoples (python community) on github or other forums .And also read other python programmer's code

I'm beginner.

Always be in the loop.

Get news once a week, and don't worry — no spam.

{{ errors }}

{{ message }}

• Help center
• We ❤️ Feedback
• Advertise / Partner
• Write for us
• Privacy Policy
• Cookie Policy
• Change Privacy Settings
• Disclosure Policy
• Terms and Conditions
• Refund Policy

Disclosure: This page may contain affliate links, meaning when you click the links and make a purchase, we receive a commission.

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. ↵

Privacy Policy

Why solve a problem twice? Design patterns let you apply existing solutions to your code

Software design patterns are like best practices employed by many experienced software developers. You can use design patterns to make your application scalable and flexible.

The most satisfying problems in software engineering are those that no one has solved before. Cracking a unique problem is something that you can use in job interviews and talk about in conferences. But the reality is that the majority of challenges you face will have already been solved. You can use those solutions to better your own software.

Software design patterns are typical solutions for the reoccurring design problems in software engineering. They're like the best practices employed by many experienced software developers. You can use design patterns to make your application scalable and flexible .

In this article, you'll discover what design patterns are and how you can apply them to develop better software applications, either from the start or through refactoring your existing code.

Note: Before learning design patterns, you should have a basic understanding of object-oriented programming.

What are design patterns?

Design patterns are solutions to commonly occurring design problems in developing flexible software using object-oriented programming . Design patterns typically use classes and objects, but you can also implement some of them using functional programming . They define how classes should be structured and how they should communicate with one another in order to solve specific problems.

Some beginners may mix up design patterns and algorithms . While an algorithm is a well-defined set of instructions, a design pattern is a higher-level description of a solution. You can implement a design pattern in various ways, whereas you must follow the specific instructions in an algorithm. They don’t solve the problem; they solve the design of the solution.

Design patterns are not blocks of code you can copy and paste to implement. They are like frameworks of solutions with which one can solve a specific problem.

Classification of design patterns

The book, Design Patterns- Elements of Reusable Object-Oriented Software written by the Gang of Four (Erich Gamma, John Vlissides, Ralph Johnson, and Richard Helm) introduced the idea of design patterns in software development. The book contains 23 design patterns to solve a variety of object-oriented design problems. These patterns are a toolbox of tried and tested solutions for various common problems that you may encounter while developing software applications.

Design patterns vary according to their complexity, level of detail, and scope of applicability for the whole system. They can be classified into three groups based on their purpose:

• Creational patterns describe various methods for creating objects to increase code flexibility and reuse.
• Structural patterns describe relations between objects and classes in making them into complex structures while keeping them flexible and efficient.
• Behavioral patterns define how objects should communicate and interact with one another.

Why should you use design patterns?

You can be a professional software developer even if you don't know a single design pattern. You may be using some design patterns without even knowing them. But knowing design patterns and how to use them will give you an idea of solving a particular problem using the best design principles of object-oriented programming. You can refactor complex objects into simpler code segments that are easy to implement, modify, test, and reuse. You don’t need to confine yourself to one specific programming language; you can implement design patterns in any programming language. They represent the idea, not the implementation.

Design patterns are all about the code. They make you follow the best design principles of software development, such as the open/closed principle ( objects should be open for extension but closed for modification ) and the single responsibility principle ( A class should have only one reason to change ). This article discusses design principles in greater detail.

You can make your application more flexible by using design patterns that break it into reusable code segments. You can add new features to your application without breaking the existing code at any time. Design patterns also enhance the readability of code; if someone wants to extend your application, they will understand the code with little difficulty.

What are useful design patterns?

Every design pattern solves a specific problem. You can use it in that particular situation. When you use design patterns in the wrong context, your code appears complex, with many classes and objects. The following are some examples of the most commonly used design patterns.

Singleton design pattern

Object oriented code has a bad reputation for being cluttered. How can you avoid creating large numbers of unnecessary objects? How can you limit the number of instances of a class? And how can a class control its instantiation?

Using a singleton pattern solves these problems. It’s a creational design pattern that describes how to define classes with only a single instance that will be accessed globally. To implement the singleton pattern, you should make the constructor of the main class private so that it is only accessible to members of the class and create a static method (getInstance) for object creation that acts as a constructor.

Here’s the implementation of the singleton pattern in Python.

The above code is the traditional way to implement the singleton pattern, but you can make it easier by using __new__ or creating a metaclass).

You should use this design pattern only when you are 100% certain that your application requires only a single instance of the main class. Singleton pattern has several drawbacks compared to other design patterns:

• You should not define something in the global scope but singleton pattern provides globally accessible instance.
• It violates the Single-responsibility principle.

Check out some more drawbacks of using a singleton pattern .

Decorator design pattern

If you’re following SOLID principles (and in general, you should), you’ll want to create objects or entities that are open for extension but closed for modification. How can you extend the functionality of an object at run-time? How can you extend an object’s behavior without affecting the other existing objects? You might consider using inheritance to extend the behavior of an existing object. However, inheritance is static. You can’t modify an object at runtime. Alternatively, you can use the decorator pattern to add additional functionality to objects (subclasses) at runtime without changing the parent class. The decorator pattern ( also known as a wrapper ) is a structural design pattern that lets you cover an existing class with multiple wrappers.

For wrappers, it employs abstract classes or interfaces through composition (instead of inheritance). In composition, one object contains an instance of other classes that implement the desired functionality rather than inheriting from the parent class. Many design patterns, including the decorator, are based on the principle of composition. Check out why you should use composition over inheritance .

The above code is the classic way of implementing the decorator pattern. You can also implement it using functions.

The decorator pattern implements the single-responsibility principle. You can split large classes into several small classes, each implementing a specific behavior and extend them afterward. Wrapping the decorators with other decorators increases the complexity of code with multiple layers. Also, it is difficult to remove a specific wrapper from the wrappers' stack.

Strategy design pattern

How can you change the algorithm at the run-time? You might tend to use conditional statements. But if you have many variants of algorithms, using conditionals makes our main class verbose. How can you refactor these algorithms to be less verbose?

The strategy pattern allows you to change algorithms at runtime. You can avoid using conditional statements inside the main class and refactor the code into separate strategy classes. In the strategy pattern, you should define a family of algorithms, encapsulate each one and make them interchangeable at runtime.

You can easily implement the strategy pattern by creating separate classes for algorithms. You can also implement different strategies as functions instead of using classes.

Here’s a typical implementation of the strategy pattern:

In the above code snippet, the client code is simple and straightforward. But in real-world application, the context changes depend on user actions, like when they click a button or change the level of the game. For example, in a chess application, the computer uses different strategy when you select the level of difficulty.

It follows the single-responsibility principle as the massive content main (context) class is divided into different strategy classes. You can add as many additional strategies as you want while keeping the main class unchanged (open/closed principle). It increases the flexibility of our application. It would be best to use this pattern when your main class has many conditional statements that switch between different variants of the same algorithm. However, if your code contains only a few algorithms, there is no need to use a strategy pattern. It just makes your code look complicated with all of the classes and objects.

State design pattern

Object oriented programming in particular has to deal with the state that the application is currently in. How can you change an object’s behavior based on its internal state? What is the best way to define state-specific behavior?

The state pattern is a behavioral design pattern. It provides an alternative approach to using massive conditional blocks for implementing state-dependent behavior in your main class. Your application behaves differently depending on its internal state, which a user can change at runtime. You can design finite state machines using the state pattern. In the state pattern, you should define separate classes for each state and add transitions between them.

State pattern follows both the single-responsibility principle as well as the open/closed principle. You can add as many states and transitions as you want without changing the main class. The state pattern is very similar to the strategy pattern, but a strategy is unaware of other strategies, whereas a state is aware of other states and can switch between them. If your class (or state machine) has a few states or rarely changes, you should avoid using the state pattern.

Command design pattern

The command pattern is a behavioral design pattern that encapsulates all the information about a request into a separate command object. Using the command pattern, you can store multiple commands in a class to use them over and over. It lets you parameterize methods with different requests, delay or queue a request’s execution, and support undoable operations. It increases the flexibility of your application.

A command pattern implements the single-responsibility principle, as you have divided the request into separate classes such as invokers, commands, and receivers. It also follows the open/closed principle. You can add new command objects without changing the previous commands.

Suppose you want to implement reversible operations (like undo/redo) using a command pattern. In that case, you should maintain a command history: a stack containing all executed command objects and the application’s state. It consumes a lot of RAM, and sometimes it is impossible to implement an efficient solution. You should use the command pattern if you have many commands to execute; otherwise, the code may become more complicated since you’re adding a separate layer of commands between senders and receivers.

According to most software design principles including the well-established SOLID principles, you should write reusable code and extendable applications. Design patterns allow you to develop flexible, scalable, and maintainable object-oriented software using best practices and design principles. All the design patterns are tried and tested solutions for various recurring problems. Even if you don't use them right away, knowing about them will give you a better understanding of how to solve different types of problems in object-oriented design. You can implement the design patterns in any programming language as they are just the description of the solution, not the implementation.

If you’re going to build large-scale applications, you should consider using design patterns because they provide a better way of developing software. If you’re interested in getting to know these patterns better, consider implementing each design pattern in your favorite programming language.

column | Coding

What’s the difference between coding and computational thinking, by sheena vaidyanathan (columnist)     aug 6, 2016.

Sheena Vaidyanathan

In my last EdSurge article, “Computer Science Goes Beyond Coding ,” I wrote about the difference between coding and computer science, to help us understand what we mean by phrases like “Teach kids to code” and “Computer science for all.”

In that article and in many other articles, there is another term that appears often: “Computational thinking.” Well, what is Computational Thinking (CT), and how does it differ from Coding and Computer Science—especially when it comes to classroom practice and instruction?

What is Computational Thinking (CT)?

My short definition: CT is a strategy that uses many of the powerful ideas in computer science to solve problems.

Let’s get into a simple, but longer definition. When given any problem, can we formulate it so that it can be solved using the power of computers? A computational thinker is one who collects data and analyzes it to understand the problem. That person then decomposes (breaks it down) into simpler problems. Instead of solving only that problem, you look for patterns, remove details and abstract so you can solve all problems of that type. You define the steps to solve the problem (the algorithm) and if possible, build a model to simulate, test and debug the solution. See my doodle below explaining these components of computational thinking.

Affordability

Not all ‘free college’ programs spark increased enrollments or more degrees, by jeffrey r. young.

How to Bring to Life the Science of Reading

By lauren keeling.

How Schools Are Holding Edtech Products to a Higher Standard

By abbie misha.

A Fifth of Students at Community College Are Still in High School

By rebecca koenig.

Journalism that ignites your curiosity about education.

EdSurge is an editorially independent project of and

• Product Index
• Write for us
• Advertising

FOLLOW EDSURGE

© 2024 All Rights Reserved

Member-only story

Programming vs Coding: Explained

Why are we still debating this.

Valleywood AI

Nerd For Tech

Introduction:

Programming is the act of providing a computer with various coded instructions to perform specific tasks . Programming is used in everything from appliances, business software, games, and also as we speak, it is being applied to medical equipment such as pacemakers and MRI machines.

Coding is the process of writing in a programming language that is then used to create these programs. The programming language can be any language — not just one specific system or type of programming language, it only has to be able to understand itself. For example, assembly languages are often considered programming languages but not all programming languages are assembly languages. Coding is typically done with a computer program called an editor that allows you to enter text commands that will then translate into programming code for whatever device the programming process applies to.

Types of Programmers:

• Systems Programmers work with programming languages that are used to build an operating system. These programming languages are typically designed for a particular purpose, such as writing device drivers or embedded applications. Systems programming is considered one of the more challenging forms of programming.

Written by Valleywood AI

Valleywood AI provides readers with the most interesting information in the fields of AI, ML, Big Data, and everything related! Find us on valleywoodai.com !

Text to speech

Programmer vs Coder: What Are Principal Differences

Welcome to the dynamic world of technology, where familiar words like “bugs,” “cookies,” and “sprints” have unique and specialized meanings. Here, the comparison of coder vs programmer goes beyond simple job titles—they represent distinct roles crucial in the tech industry. Grasping these differences is particularly important for managers who are new to this field, as understanding these nuances can prevent the misalignment of skills and job roles.

Although people often use the terms programmer vs coder interchangeably, there is a significant difference between the two. Programmers focus on the logical structure and design of a project, carefully planning the pathway to the final product. Coders, on the other hand, take these plans and convert them into executable scripts that computers can understand and process. Both roles are essential in creating successful software, with each bringing unique skills to the table.

So, what distinguishes a coder from a programmer, and how do you determine which one your project requires? This guide aims to clarify these roles, highlighting the unique contributions each makes to a software development team . By the end, you’ll have a better understanding of whom to hire, bringing your next tech idea to life.

Table of Contents

What Is a Programmer?

In software development, a programmer is much like an architect. They don’t just build; they envision and innovate throughout the development journey. These strategists and visionaries shape complex software systems, blending their coding prowess with critical problem-solving and design skills.

Fluent in numerous programming languages, programmers exhibit remarkable adaptability, navigating effortlessly through Python , Java, C++, and more. Their responsibilities are diverse and expansive:

• Project management. Guiding the development process and making pivotal strategic decisions.
• Maintenance and optimization. Boosting software performance and integrating new features after deployment.
• Testing and quality assurance. Ensuring the software meets high-quality standards and specifications.
• Problem-solving and debugging. Addressing complex issues and refining code functionality.
• Advanced code development. Creating sophisticated algorithms and data structures.
• System design and analysis. Designing the software’s architecture and evaluating project requirements.

What Is a Coder?

A coder meticulously translates the visions and designs of others into operational software, much like a craftsman bringing blueprints to life. In code, these artisans turn abstract concepts into functional programs using languages such as Python , Java, HTML/CSS, JavaScript , and SQL.

Coders often specialize in a particular programming language, excelling in tasks that are foundational yet straightforward. They are typically the first touchpoint in the software creation process, focusing on several key areas:

• Collaboration. Coders work synergistically with seasoned developers or project managers, aligning their contributions with the overall project vision.
• Maintenance. They update and refine existing software to adapt to evolving needs.
• Debugging and testing. Coders identify and fix code issues, ensuring the software operates smoothly.
• Code Implementation. They translate requirements and designs into functional code, turning ideas into executable programs.

Programmer vs Coder: What are the Differences

If you want to hire the right coder or programmer for your development team, it’s crucial to understand each one’s distinct roles and skills.

One key difference between a computer programmer vs coder is the scope of their responsibilities. Coding involves translating programming languages into efficient, error-free code to perform specific tasks, whereas programming encompasses the design, logic, and flow of the entire codebase. Essentially, coding is a subset of programming; all coding is part of programming, but not all programming involves coding.

Coders focus on writing code. Their main task is to input scripts into a product, ensuring the processor executes the required commands without delving into the broader aspects of product design.

To be a proficient coder, one must be fluent in programming languages. Whether it’s a functional language like SQL or an object-oriented language like Python, coders need to master the syntax, commands, classes, and basic logic of at least one language. Their responsibilities typically include:

• Quality analysis
• Code optimization
• Utilizing existing language libraries to avoid redundant coding

Many coders specialize in one language, but due to similarities between languages, some become proficient in several. To ensure you hire top-quality talent, leverage coding assessment tools to evaluate an applicant’s expertise in their chosen language.

Related articles:

• How to Extend Your Software Development Team
• A Guide on How to Recruit Software Developers: Outsource Coders vs. Freelancers
• Software Architect vs Software Engineer: Core Differences
• 11 Software Developer Soft Skills Every Programmer Needs to Succeed
• Who Does What? Understanding Roles in a Software Development Startup

Key Differences Between a Coder vs Programmer

If coders write code and programmers plan products, it’s easy to see why there might be some confusion between the two roles. However, there’s a significant amount of overlap. For instance, both coders and programmers need a solid understanding of a language’s logic to ensure that their final code can be tested and debugged. Coders must be fluent in their chosen language, and programmers often share this requirement. The shift from the title “professional coder” to “junior programmer” or “junior developer” further blurs the lines.

What are the differences of coders and programmers in software development? Let’s explore them.

Programmers The role of a programmer goes beyond just writing code; it starts with planning all the necessary functionalities and frameworks within a product’s design . Before diving into coding, the product’s logic and features must be thoroughly outlined.

Take, for instance, a data-mining app designed to scrape the web for specific statistics and organize them into designated categories. Before touching a keyboard, a programmer might draft a neural network blueprint that directs data into separate repositories, with actions to be performed on the data thereafter. This process requires coding knowledge, but the primary task for a programmer is to plan how the product will function meticulously.

Coders You can meticulously plan a product, but eventually, you have to write the code. A coder’s role in a project is to handle the technical aspects by inputting the actual code in the required language. Their focus is on the execution, not the reasoning or methodology behind it.

For instance, once the data-mining app is outlined—and at various stages during development—the coder’s task is to input the script to make it operational. They might write their own code or use a module from the language library to accomplish the task, testing, and debugging as they go. Coders are so focused on compiling machine code that they might not even know the specific function of the snippet they’re writing; their job is simply to code.

The Skills Needed

Coders and programmers each have distinct roles, which require them to develop unique skill sets. While some of these skills are honed through specific training, others stem from inherently different ways of thinking.

Programmers’ Skills A programmer’s primary focus is planning, with their skills mainly revolving around logic and design and a bit of emphasis on UI/UX. They typically have a solid understanding of algorithms and discrete math, which aids them in creating decision trees, truth tables, and workflows that form the core structure of a product. While knowing the specifics of coding is important, a strategic and thoughtful approach to design takes precedence. This is why many programming courses emphasize developing these skills before introducing any actual coding.

Coders’ Skills Since a coder’s main job is to write the scripts that keep a product running smoothly, their focus is more on language expertise than design. A coder needs to be fluent in their programming language, understanding all the commands, keywords, strings, functions, and logic required to ensure everything works correctly.

A skilled programmer should be capable of writing clean, straightforward, and reliable code. They should also be adept at testing and debugging and know where to find templates from libraries and coding communities to speed up their work. Additionally, knowing how to optimize and organize code is crucial. Soft skills like organization and attention to detail are also invaluable.

The Tools Used

Having unique roles and skill sets means that coders and programmers often rely on different tools for their work, although there’s some overlap in what they use.

Programmers’ Tools Since programmers need to outline the framework of their projects skillfully, they rely on various tools to help them implement their devised workflows. Some of the most common tools programmers use include:

• Text editors
• Agile development frameworks
• Wireframing tools
• Productivity trackers
• Platform-as-a-service (PaaS) apps

While their language proficiency means they might use some of the same tools as coders, programmers primarily utilize apps that assist in planning and tracking their projects throughout the design journey rather than executing code.

Coders’ Tools Since coders are responsible for creating high-quality scripts, they rely on tools designed to help them translate commands accurately and efficiently. Some essential tools coders use include:

• Source code editors
• Interpreters
• Online communities
• Coding automation solutions
• Low-code/no-code platforms

Efficient task management is crucial for coders and programmers, so they often utilize productivity trackers and agile development frameworks. However, the primary software tools in a coder’s toolkit assist in writing clean, efficient code.

Deciding Which One You Need for Your Projects

When planning to hire for your project, how do you decide between a coder, a programmer, and a developer? According to Zippia, the Department of Labor estimates that hiring the wrong employee can cost up to 30% of their annual salary , or even more, making it crucial to get the right fit. Here are some key considerations to help you choose the right talent:

• Skills. Ensure your candidate has the necessary skills to contribute effectively. Testing their competencies can reveal if they are equipped to handle your project’s demands.
• Scope. Clarify your needs—do you require a coder to write or test scripts, a programmer to design the app’s functionality, or a developer to manage the entire process? Defining this will streamline your hiring process.
• Tools. Identify the tools your new hire needs to be proficient in. Their experience with your current tech stack will be vital for their success.

Navigating the tech industry’s complexities is no small feat, especially when finding the right talent. That’s where IntelliSoft comes in. As a distinguished software outsourcing company, we specialize in connecting you with top-tier developers and programmers perfectly suited to your project’s needs. With a vast global network, we ensure you access the best software development expertise available. Whether you need a single specialist, a dedicated team , or a full R&D center, IntelliSoft is your gateway to scaling your development capabilities efficiently.

Contact us today and see how our tailored solutions can transform your project and drive your success forward.

What are the key differences between a coder vs programmer vs developer vs software engineer?

In software development, the titles coder vs programmer vs developer vs software engineer each carry unique connotations reflecting varying levels of expertise and scope of work. Understanding these distinctions can help you navigate the tech landscape more effectively.Coder Consider coders the entry-level foot soldiers in the programming world. Their primary task is to write code according to specific guidelines or instructions. They convert logical solutions into a programming language but aren't usually involved in higher-level design or architectural decisions. For example, a coder might take a pre-defined algorithm and implement it in Python, ensuring it functions correctly without delving into why that algorithm was chosen or how it fits into the larger system.Programmer Programmers delve a bit deeper than coders. They not only write code but also have a solid understanding of algorithms and the logic behind them. Their role often encompasses various stages of software development, with a significant focus on debugging and compiling code. For instance, a programmer might be tasked with optimizing an algorithm for better performance, identifying bottlenecks, and refining the code to enhance efficiency.Developer Developers wear multiple hats. They are responsible for designing and writing software, which involves integrating user requirements into functional applications. Developers often tackle more complex tasks than coders and programmers, taking on some project management and software design responsibilities. For example, a developer might work on creating a user-friendly interface for a mobile app, ensuring it meets user needs and integrates smoothly with backend services.Software Engineer Software engineers apply engineering principles to software development. Their role is comprehensive, encompassing system design, architecture, and the broader aspects of scalability, efficiency, and robustness. Software engineers oversee the entire development process, often leading teams and coordinating efforts to address various aspects of software systems, from initial design to final implementation. For instance, a software engineer might design a scalable cloud-based system, considering factors like load balancing, data storage, and network security

How does the role of a programmer vs coder vs developer compare in the tech industry?

The roles of programmer vs coder vs developer in the tech industry, though closely related, differ in their scope, responsibilities, and often the complexity of tasks they handle:Coder. A coder's main focus is writing code. They take the requirements and preliminary designs provided by others and transform them into functional software. Coders usually concentrate on a specific module or part of an application, working meticulously to ensure their code performs as intended. Their role is crucial in the early stages of software development, laying the groundwork for more complex system integration. Programmer. A programmer's responsibilities extend beyond just writing code. They are involved in various stages of the software development process, including debugging errors, maintaining existing systems, and testing software to ensure it’s reliable and functional. Programmers take initial plans and blueprints and turn them into working technology. They play a pivotal role throughout the development lifecycle, ensuring the software operates efficiently and meets quality standards. Developer. Developers are integral to the entire lifecycle of software projects, from initial conception through active development, deployment, and ongoing maintenance. They make high-level decisions influencing project outcomes, manage project progression, and ensure the final products meet strategic business goals. Developers typically possess a robust blend of skills in programming, design, and analysis, allowing them to write code, lead projects, and direct teams. Their role is crucial in ensuring software solutions are both effective and aligned with the broader objectives of the organization.

What are the salary differences in coder vs programmer salaries across various regions?

The coder vs programmer salary varies widely depending on the region, heavily influenced by local cost of living, demand for tech skills, and overall economic conditions.General salary trends Software engineer salaries can vary significantly across different areas. Higher salaries are often found in countries with higher living costs and more established tech industries. Generally, programmers, who usually have broader responsibilities than coders, may earn higher salaries due to their extensive skill sets and roles in project execution.Specific roles and salaries For example, Ruby developers' salaries can range from $26,000 to $115,000, depending on the country. This variation reflects a broader trend where technical roles requiring more specialized or advanced skills, such as programming, often have higher earning potential compared to basic coding positions.Comparison across countries In regions like the US and Europe, salaries are typically higher compared to countries like India. This disparity is also evident within specific categories of coding and programming roles, where more developed tech ecosystems and higher costs of living drive salaries higher.

In what contexts might the terms programmer vs developer vs coder be used interchangeably?

The titles "programmer," "developer," and "coder" frequently blend in conversation and across various settings, often due to a casual or undefined distinction between their roles. Here's where you might notice these terms being used somewhat interchangeably:In the workplace. Particularly in smaller businesses or startups, you might hear any of these terms used to describe someone who programs, no matter the complexity or breadth of their duties. During job recruitment. Job postings might use any of these titles without distinguishing between them. This can sometimes confuse job seekers who might have different expectations of what each role entails. In education and training. Whether it’s during a coding bootcamp, a class, or an online tutorial, these terms are frequently used loosely. They might refer both to the people teaching the skills and the skills participants hope to gain. In media and cultural discussions. Articles and blogs about tech often don't differentiate between these titles, using them as general terms for anyone involved in software development. In casual tech talks. At meetups or on forums where tech enthusiasts gather, these terms can be thrown around interchangeably due to the shared skill sets of the roles and the laid-back nature of the conversations.

What distinguishes a developer vs programmer vs coder in a project team?

In a team setting where everyone is working on software projects, you might hear the terms developer vs programmer vs coder used like they mean the same thing. However, each role has its own specific responsibilities:Coder This is usually an entry-level job. Coders turn problem-solving ideas and algorithms into actual computer code. Their main job is to write code that meets the basic needs of the project, usually following specific instructions without much involvement in the bigger design or architecture choices.Programmer A programmer does more than just write code. They also find and fix bugs, test the software, and keep it running smoothly. This position is generally for someone with more experience than a coder. It includes more detailed work, such as combining different software parts and making sure everything works well together.Developer Developers take care of a software project from start to finish. They don't just write code; they also plan, design, test, and maintain systems. They're often involved in making major design decisions and setting coding standards. Developers frequently interact with clients or main stakeholders to gather project requirements, offer solutions, and keep an eye on the project's progress. Depending on the type of project, developers might focus on specific areas like web, software, or mobile apps, each needing different skills and expertise.

What are the main distinctions in the processes of coding vs. programming vs development?

The terms coding, programming, and development are often used interchangeably in software creation, but they actually refer to different parts of the process:Coding. This is the basic task in software building. It involves converting logic and algorithms into a language computers can understand. Coders handle the details, often working on specific functions or features that someone else has outlined. Essentially, it's about writing code following specific instructions or guidelines. Programming. Programming goes beyond just writing code. It includes planning, problem-solving, and integrating various software elements to work together smoothly. Programmers look at the bigger picture, address and fix bugs, and implement solutions to create a functional application. Development. This term covers the entire software creation process, from initial planning and design to programming, testing, and maintenance. Developers oversee the project from start to finish, ensuring the final product meets user needs and business goals. They often work on multiple aspects of a project, ensuring everything comes together to create a successful end product. H3 How do the skillsets and responsibilities of a coder, programmer, and developer overlap and differ? The roles of coder, programmer, and developer in software development have both overlapping skill sets and distinct responsibilities:Coder A coder is primarily focused on the intricate task of writing code, serving as the craftsman who converts complex algorithms and detailed technical specifications into clear, executable instructions. This role demands a profound knowledge of both the syntax (the structural rules of programming) and semantics (the meanings of commands) of various programming languages. Although coders are deeply involved in the technical facets of development, their responsibilities generally do not extend to the broader elements of software design or the development of overarching problem-solving strategies. Instead, coders are usually tasked with managing specific, isolated tasks or modules within the framework of a larger project, often under the guidance of more senior programming staff.Programmer Programmers embody a more expansive skill set than coders, engaging not only in writing code but also in debugging, testing, and maintaining existing systems to ensure functionality and efficiency. This role requires robust problem-solving abilities and a capacity to think algorithmically to address and rectify issues as they arise during development. Programmers typically take on more complex and challenging projects compared to coders. Their role necessitates a holistic understanding of software development, necessitating the integration of various software components into a cohesive system. This might involve frequent collaboration with other team members across different stages of the software lifecycle.Developer The developer role is the most comprehensive, covering all aspects of software development from conception to deployment. Developers are responsible for gathering requirements, designing software architectures, coding, testing, and maintenance. They need a strong grasp of technical skills, project management, and possibly customer interaction. Depending on the complexity and scope of the project, developers might also act as project leaders or architects.

About Kosta Mitrofanskiy

Related Posts

• Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers
• Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand
• OverflowAI GenAI features for Teams
• OverflowAPI Train & fine-tune LLMs
• Labs The future of collective knowledge sharing
• About the company Visit the blog

Collectives™ on Stack Overflow

Find centralized, trusted content and collaborate around the technologies you use most.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

Get early access and see previews of new features.

What is the difference between bottom-up and top-down?

The bottom-up approach (to dynamic programming) consists in first looking at the "smaller" subproblems, and then solve the larger subproblems using the solution to the smaller problems.

The top-down consists in solving the problem in a "natural manner" and check if you have calculated the solution to the subproblem before.

I'm a little confused. What is the difference between these two?

• dynamic-programming
• memoization

• 7 Related: stackoverflow.com/questions/6184869/… –  aioobe Commented Feb 25, 2015 at 21:12

9 Answers 9

rev4: A very eloquent comment by user Sammaron has noted that, perhaps, this answer previously confused top-down and bottom-up. While originally this answer (rev3) and other answers said that "bottom-up is memoization" ("assume the subproblems"), it may be the inverse (that is, "top-down" may be "assume the subproblems" and "bottom-up" may be "compose the subproblems"). Previously, I have read on memoization being a different kind of dynamic programming as opposed to a subtype of dynamic programming. I was quoting that viewpoint despite not subscribing to it. I have rewritten this answer to be agnostic of the terminology until proper references can be found in the literature. I have also converted this answer to a community wiki. Please prefer academic sources. List of references: {Web: 1 , 2 } {Literature: 5 }

Dynamic programming is all about ordering your computations in a way that avoids recalculating duplicate work. You have a main problem (the root of your tree of subproblems), and subproblems (subtrees). The subproblems typically repeat and overlap .

For example, consider your favorite example of Fibonnaci. This is the full tree of subproblems, if we did a naive recursive call:

(In some other rare problems, this tree could be infinite in some branches, representing non-termination, and thus the bottom of the tree may be infinitely large. Furthermore, in some problems you might not know what the full tree looks like ahead of time. Thus, you might need a strategy/algorithm to decide which subproblems to reveal.)

Memoization, Tabulation

There are at least two main techniques of dynamic programming which are not mutually exclusive:

Memoization - This is a laissez-faire approach: You assume that you have already computed all subproblems and that you have no idea what the optimal evaluation order is. Typically, you would perform a recursive call (or some iterative equivalent) from the root, and either hope you will get close to the optimal evaluation order, or obtain a proof that you will help you arrive at the optimal evaluation order. You would ensure that the recursive call never recomputes a subproblem because you cache the results, and thus duplicate sub-trees are not recomputed.

• example: If you are calculating the Fibonacci sequence fib(100) , you would just call this, and it would call fib(100)=fib(99)+fib(98) , which would call fib(99)=fib(98)+fib(97) , ...etc..., which would call fib(2)=fib(1)+fib(0)=1+0=1 . Then it would finally resolve fib(3)=fib(2)+fib(1) , but it doesn't need to recalculate fib(2) , because we cached it.
• This starts at the top of the tree and evaluates the subproblems from the leaves/subtrees back up towards the root.

Tabulation - You can also think of dynamic programming as a "table-filling" algorithm (though usually multidimensional, this 'table' may have non-Euclidean geometry in very rare cases*). This is like memoization but more active, and involves one additional step: You must pick, ahead of time, the exact order in which you will do your computations. This should not imply that the order must be static, but that you have much more flexibility than memoization.

• example: If you are performing fibonacci, you might choose to calculate the numbers in this order: fib(2) , fib(3) , fib(4) ... caching every value so you can compute the next ones more easily. You can also think of it as filling up a table (another form of caching).
• I personally do not hear the word 'tabulation' a lot, but it's a very decent term. Some people consider this "dynamic programming".
• Before running the algorithm, the programmer considers the whole tree, then writes an algorithm to evaluate the subproblems in a particular order towards the root, generally filling in a table.
• *footnote: Sometimes the 'table' is not a rectangular table with grid-like connectivity, per se. Rather, it may have a more complicated structure, such as a tree, or a structure specific to the problem domain (e.g. cities within flying distance on a map), or even a trellis diagram, which, while grid-like, does not have a up-down-left-right connectivity structure, etc. For example, user3290797 linked a dynamic programming example of finding the maximum independent set in a tree , which corresponds to filling in the blanks in a tree.

(At it's most general, in a "dynamic programming" paradigm, I would say the programmer considers the whole tree, then writes an algorithm that implements a strategy for evaluating subproblems which can optimize whatever properties you want (usually a combination of time-complexity and space-complexity). Your strategy must start somewhere, with some particular subproblem, and perhaps may adapt itself based on the results of those evaluations. In the general sense of "dynamic programming", you might try to cache these subproblems, and more generally, try avoid revisiting subproblems with a subtle distinction perhaps being the case of graphs in various data structures. Very often, these data structures are at their core like arrays or tables. Solutions to subproblems can be thrown away if we don't need them anymore.)

[Previously, this answer made a statement about the top-down vs bottom-up terminology; there are clearly two main approaches called Memoization and Tabulation that may be in bijection with those terms (though not entirely). The general term most people use is still "Dynamic Programming" and some people say "Memoization" to refer to that particular subtype of "Dynamic Programming." This answer declines to say which is top-down and bottom-up until the community can find proper references in academic papers. Ultimately, it is important to understand the distinction rather than the terminology.]

Pros and cons

Ease of coding.

Memoization is very easy to code (you can generally* write a "memoizer" annotation or wrapper function that automatically does it for you), and should be your first line of approach. The downside of tabulation is that you have to come up with an ordering.

*(this is actually only easy if you are writing the function yourself, and/or coding in an impure/non-functional programming language... for example if someone already wrote a precompiled fib function, it necessarily makes recursive calls to itself, and you can't magically memoize the function without ensuring those recursive calls call your new memoized function (and not the original unmemoized function))

Recursiveness

Note that both top-down and bottom-up can be implemented with recursion or iterative table-filling, though it may not be natural.

Practical concerns

With memoization, if the tree is very deep (e.g. fib(10^6) ), you will run out of stack space, because each delayed computation must be put on the stack, and you will have 10^6 of them.

Either approach may not be time-optimal if the order you happen (or try to) visit subproblems is not optimal, specifically if there is more than one way to calculate a subproblem (normally caching would resolve this, but it's theoretically possible that caching might not in some exotic cases). Memoization will usually add on your time-complexity to your space-complexity (e.g. with tabulation you have more liberty to throw away calculations, like using tabulation with Fib lets you use O(1) space, but memoization with Fib uses O(N) stack space).

Advanced optimizations

If you are also doing a extremely complicated problems, you might have no choice but to do tabulation (or at least take a more active role in steering the memoization where you want it to go). Also if you are in a situation where optimization is absolutely critical and you must optimize, tabulation will allow you to do optimizations which memoization would not otherwise let you do in a sane way. In my humble opinion, in normal software engineering, neither of these two cases ever come up, so I would just use memoization ("a function which caches its answers") unless something (such as stack space) makes tabulation necessary... though technically to avoid a stack blowout you can 1) increase the stack size limit in languages which allow it, or 2) eat a constant factor of extra work to virtualize your stack (ick), or 3) program in continuation-passing style, which in effect also virtualizes your stack (not sure the complexity of this, but basically you will effectively take the deferred call chain from the stack of size N and de-facto stick it in N successively nested thunk functions... though in some languages without tail-call optimization you may have to trampoline things to avoid a stack blowout).

More complicated examples

Here we list examples of particular interest, that are not just general DP problems, but interestingly distinguish memoization and tabulation. For example, one formulation might be much easier than the other, or there may be an optimization which basically requires tabulation:

• the algorithm to calculate edit-distance[ 4 ], interesting as a non-trivial example of a two-dimensional table-filling algorithm
• 3 @coder000001: for python examples, you could google search for python memoization decorator ; some languages will let you write a macro or code which encapsulates the memoization pattern. The memoization pattern is nothing more than "rather than calling the function, look up the value from a cache (if the value is not there, compute it and add it to the cache first)". –  ninjagecko Commented Dec 12, 2012 at 1:40
• 25 I don't see anybody mentioning this but I think another advantage of Top down is that you will only build the look-up table/cache sparsely. (ie you fill in the values where you actually need them). So this might be the pros in addition to easy coding. In other words, top down might save you actual running time since you don't compute everything (you might have tremendously better running time but same asymptotic running time though). Yet it requires additional memory to keep the additional stack frames (again, memory consumption 'may' (only may) double but asymptotically it is the same. –  InformedA Commented Jul 7, 2014 at 11:13
• 3 I am under the impression that top-down approaches that cache solutions to overlapping subproblems is a technique called memoization . A bottom up technique that fills a table and also avoids recomputing overlapping subproblems is referred to as tabulation . These techniques can be employed when using dynamic programming , which refers to solving subproblems to solve a much bigger problem. This seems contradictory with this answer, where this answer uses dynamic programming instead of tabulation in many places. Who is correct? –  Sammaron Commented Sep 7, 2015 at 20:07
• 1 @Sammaron: hmm, you make a good point. I should have perhaps checked my source on Wikipedia, which I cannot find. Upon checking cstheory.stackexchange a bit, I now agree "bottom-up" would imply the bottom is known beforehand (tabulation), and "top-down" is you assume solution to subproblems/subtrees. At the time I found the term ambiguous, and I interpreted the phrases in the dual view ("bottom-up" you assume solution to subproblems and memorize, "top-down" you know which subproblems you are about and can tabulate). I will attempt to address this in an edit. –  ninjagecko Commented Sep 8, 2015 at 12:12
• 1 @mgiuffrida: Stack space is sometimes treated differently depending on the programming language. For example in python, trying to perform a memoized recursive fib will fail for say fib(513) . The overloaded terminology I feel is getting in the way here. 1) You can always throw away subproblems you no longer need. 2) You can always avoid calculating subproblems you don't need. 3) 1 and 2 may be much harder to code without an explicit data structure to store subproblems in, OR, harder if control flow must weave between function calls (you might need state or continuations). –  ninjagecko Commented Dec 3, 2015 at 20:00

Top down and bottom up DP are two different ways of solving the same problems. Consider a memoized (top down) vs dynamic (bottom up) programming solution to computing fibonacci numbers.

I personally find memoization much more natural. You can take a recursive function and memoize it by a mechanical process (first lookup answer in cache and return it if possible, otherwise compute it recursively and then before returning, you save the calculation in the cache for future use), whereas doing bottom up dynamic programming requires you to encode an order in which solutions are calculated, such that no "big problem" is computed before the smaller problem that it depends on.

• 3 Ah, now I see what "top-down" and "bottom-up" mean; it is in fact just referring to memoization vs DP. And to think I was the one who edited the question to mention DP in the title... –  ninjagecko Commented May 28, 2011 at 22:39
• what's the runtime of memoized fib v/s normal recursive fib? –  Siddhartha Commented Oct 3, 2012 at 23:26
• 1 Yeah it is linear! I drew out the recursion tree and saw what calls could be avoided and realized the memo_fib(n - 2) calls would be all avoided after the first call to it, and so all the right branches of the recursion tree would be cut off and it'll reduce to linear. –  Siddhartha Commented Oct 4, 2012 at 0:25
• 2 Since DP involves essentially building up a results table where each result is computed at most once, one simple way to visualize a DP algorithm's runtime is to see how large the table is. In this case, it's of size n (one result per input value) so O(n). In other cases, it could be an n^2 matrix, resulting in O(n^2), etc. –  Johnson Wong Commented Jan 14, 2015 at 7:15
• 1 Yeah, pre-populating the cache to get rid of the base case works fine and simplifies the code. When I memoize functions, I tend to like to first write it recursively and then mechanically memoize it. –  Rob Neuhaus Commented Feb 25, 2021 at 4:40

A key feature of dynamic programming is the presence of overlapping subproblems . That is, the problem that you are trying to solve can be broken into subproblems, and many of those subproblems share subsubproblems. It is like "Divide and conquer", but you end up doing the same thing many, many times. An example that I have used since 2003 when teaching or explaining these matters: you can compute Fibonacci numbers recursively.

Use your favorite language and try running it for fib(50) . It will take a very, very long time. Roughly as much time as fib(50) itself! However, a lot of unnecessary work is being done. fib(50) will call fib(49) and fib(48) , but then both of those will end up calling fib(47) , even though the value is the same. In fact, fib(47) will be computed three times: by a direct call from fib(49) , by a direct call from fib(48) , and also by a direct call from another fib(48) , the one that was spawned by the computation of fib(49) ... So you see, we have overlapping subproblems .

Great news: there is no need to compute the same value many times. Once you compute it once, cache the result, and the next time use the cached value! This is the essence of dynamic programming. You can call it "top-down", "memoization", or whatever else you want. This approach is very intuitive and very easy to implement. Just write a recursive solution first, test it on small tests, add memoization (caching of already computed values), and --- bingo! --- you are done.

Usually you can also write an equivalent iterative program that works from the bottom up, without recursion. In this case this would be the more natural approach: loop from 1 to 50 computing all the Fibonacci numbers as you go.

In any interesting scenario the bottom-up solution is usually more difficult to understand. However, once you do understand it, usually you'd get a much clearer big picture of how the algorithm works. In practice, when solving nontrivial problems, I recommend first writing the top-down approach and testing it on small examples. Then write the bottom-up solution and compare the two to make sure you are getting the same thing. Ideally, compare the two solutions automatically. Write a small routine that would generate lots of tests, ideally -- all small tests up to certain size --- and validate that both solutions give the same result. After that use the bottom-up solution in production, but keep the top-bottom code, commented out. This will make it easier for other developers to understand what it is that you are doing: bottom-up code can be quite incomprehensible, even you wrote it and even if you know exactly what you are doing.

In many applications the bottom-up approach is slightly faster because of the overhead of recursive calls. Stack overflow can also be an issue in certain problems, and note that this can very much depend on the input data. In some cases you may not be able to write a test causing a stack overflow if you don't understand dynamic programming well enough, but some day this may still happen.

Now, there are problems where the top-down approach is the only feasible solution because the problem space is so big that it is not possible to solve all subproblems. However, the "caching" still works in reasonable time because your input only needs a fraction of the subproblems to be solved --- but it is too tricky to explicitly define, which subproblems you need to solve, and hence to write a bottom-up solution. On the other hand, there are situations when you know you will need to solve all subproblems. In this case go on and use bottom-up.

I would personally use top-bottom for Paragraph optimization a.k.a the Word wrap optimization problem (look up the Knuth-Plass line-breaking algorithms; at least TeX uses it, and some software by Adobe Systems uses a similar approach). I would use bottom-up for the Fast Fourier Transform .

• Hello!!! I want to determine if the following propositions are right. - For a Dynamic Programming algorithm, the computation of all the values with bottom-up is asymptotically faster then the use of recursion and memoization. - The time of a dynamic algorithm is always Ο(Ρ) where Ρ is the number of subproblems. - Each problem in NP can be solved in exponential time. –  Mary Star Commented Mar 28, 2015 at 19:12
• What could I say about the above propositions? Do you have an idea? @osa –  Mary Star Commented Mar 28, 2015 at 19:14
• @evinda, (1) is always wrong. It is either the same or asymptotically slower (when you don't need all subproblems, recursion can be faster). (2) is only right if you can solve every subproblem in O(1). (3) is kind of right. Each problem in NP can be solved in polynomial time on a nondeterministic machine (like a quantum computer, that can do multiple things simultaneously: have its cake, and simultaneously eat it, and trace both results). So in a sense, each problem in NP can be solved in exponential time on a regular computer. SIde note: everything in P is also in NP. E.g. adding two integers –  Sergey Orshanskiy Commented Mar 30, 2015 at 17:17

Lets take fibonacci series as an example

Another way to put it,

In case of first five fibonacci number

Now lets take a look of recursive Fibonacci series algorithm as an example

Now if we execute this program with following commands

if we closely look into the algorithm, in-order to generate fifth number it requires 3rd and 4th numbers. So my recursion actually start from top(5) and then goes all the way to bottom/lower numbers. This approach is actually top-down approach.

To avoid doing same calculation multiple times we use Dynamic Programming techniques. We store previously computed value and reuse it. This technique is called memoization. There are more to Dynamic programming other then memoization which is not needed to discuss current problem.

Lets rewrite our original algorithm and add memoized techniques.

And we execute this method like following

This solution is still top-down as algorithm start from top value and go to bottom each step to get our top value.

But, question is, can we start from bottom, like from first fibonacci number then walk our way to up. Lets rewrite it using this techniques,

Now if we look into this algorithm it actually start from lower values then go to top. If i need 5th fibonacci number i am actually calculating 1st, then second then third all the way to up 5th number. This techniques actually called bottom-up techniques.

Last two, algorithms full-fill dynamic programming requirements. But one is top-down and another one is bottom-up. Both algorithm has similar space and time complexity.

• Can we say bottom-up approach is often implemented in a non-recursive way ? –  Lewis Chan Commented Feb 14, 2019 at 5:59
• Nope, you can convert any loop logic to recursion –  Ashvin Sharma Commented Aug 27, 2019 at 7:24

Dynamic programming problems can be solved using either bottom-up or top-down approaches.

Generally, the bottom-up approach uses the tabulation technique, while the top-down approach uses the recursion (with memorization) technique.

But you can also have bottom-up and top-down approaches using recursion as shown below.

Bottom-Up : Start with the base condition and pass the value calculated until now recursively. Generally, these are tail recursions.

Top-Down : Start with the final condition and recursively get the result of its sub-problems.

• no memoization or tabulation in 2nd approach? –  Pradeep Commented Feb 21, 2021 at 8:29
• @Pradeep, Of course, you can use memoization and/or tabulation with both approaches. –  Ashwin Commented Feb 21, 2021 at 8:55

Dynamic Programming is often called Memoization!

1.Memoization is the top-down technique(start solving the given problem by breaking it down) and dynamic programming is a bottom-up technique(start solving from the trivial sub-problem, up towards the given problem)

2.DP finds the solution by starting from the base case(s) and works its way upwards. DP solves all the sub-problems, because it does it bottom-up

Unlike Memoization, which solves only the needed sub-problems

DP has the potential to transform exponential-time brute-force solutions into polynomial-time algorithms.

DP may be much more efficient because its iterative

On the contrary, Memoization must pay for the (often significant) overhead due to recursion.

To be more simple, Memoization uses the top-down approach to solve the problem i.e. it begin with core(main) problem then breaks it into sub-problems and solve these sub-problems similarly. In this approach same sub-problem can occur multiple times and consume more CPU cycle, hence increase the time complexity. Whereas in Dynamic programming same sub-problem will not be solved multiple times but the prior result will be used to optimize the solution.

• 6 that's not true, memoization uses a cache which will help you save the time complexity to the same as DP –  InformedA Commented Jul 7, 2014 at 11:05

Simply saying top down approach uses recursion for calling Sub problems again and again where as bottom up approach use the single without calling any one and hence it is more efficient.

Following is the DP based solution for Edit Distance problem which is top down. I hope it will also help in understanding the world of Dynamic Programming:

You can think of its recursive implementation at your home. It's quite good and challenging if you haven't solved something like this before.

nothing to be confused about... you usually learn the language in bottom-up manner (from basics to more complicated things), and often make your project in top-down manner (from overall goal & structure of the code to certain pieces of implementations)

If concerning logical code examples - here

Your Answer

Reminder: Answers generated by artificial intelligence tools are not allowed on Stack Overflow. Learn more

Sign up or log in

Post as a guest.

Required, but never shown

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy .

Not the answer you're looking for? Browse other questions tagged dynamic-programming difference memoization or ask your own question .

• The Overflow Blog
• Where does Postgres fit in a world of GenAI and vector databases?
• Featured on Meta
• We've made changes to our Terms of Service & Privacy Policy - July 2024
• Bringing clarity to status tag usage on meta sites
• What does a new user need in a homepage experience on Stack Overflow?
• Feedback requested: How do you use tag hover descriptions for curating and do...
• Staging Ground Reviewer Motivation

Hot Network Questions

• Topology on a module over a topological ring
• Walk or Drive to school?
• Is it advisable to contact faculty members at U.S. universities prior to submitting a PhD application?
• Do metal objects attract lightning?
• Passport Carry in Taiwan
• What to do when 2 light switches are too far apart for the light switch cover plate?
• The answer is not wrong
• Why is PUT Request not allowed by default in OWASP CoreRuleSet
• Is it possible to have a planet that's gaslike in some areas and rocky in others?
• What is an intuitive way to rename a column in a Dataset?
• Do the amplitude and frequency of gravitational waves emitted by binary stars change as the stars get closer together?
• Using "no" at the end of a statement instead of "isn't it"?
• The size of elementary particles
• What rule or standard is used to assign a SOT number to a chip housing?
• Series with odd numbers
• Does Vexing Bauble counter taxed 0 mana spells?
• Why there is no article after 'by'?
• about flag changes in 16-bit calculations on the MC6800
• How to remove obligation to run as administrator in Windows?
• What prevents a browser from saving and tracking passwords entered to a site?
• Relation between stopping times and their filtrations
• Deploying contracts from safe wallet
• My school wants me to download an SSL certificate to connect to WiFi. Can I just avoid doing anything private while on the WiFi?
• Why do National Geographic and Discovery Channel broadcast fake or pseudoscientific programs?

Coding vs. Programming vs. Computational Thinking – What’s The Difference?

August 18, 2016

#Classroom Computational Thinking Digital Literacy programming

The terms ‘coding’ and ‘programming’ are often used interchangeably, and some parents may wonder if there are any differences between the two.

Before I answer that question, I would like to throw ‘computational thinking’ into the fray and spend the rest of this blog post dissecting these three terms so parents get a better understanding of what each one means.

Coding versus Programming

This is one of those questions that splits opinions. There’s a camp out there which advocates that coding is low level work – basically translating logic into code (another word for programming language) so that your application can run. Programming is higher order work – designing logic to solve problems. This camp can basically be summed up in four words – ‘Coders cheap. Programmers expensive.’

The other camp, unsurprisingly, argues there is no difference between coding and programming. Engineers, coders, programmers, hackers, developers (devs) are all pretty much the same thing. It’s just branding.

I personally don’t see much difference between coding and programming and tend to use the two words interchangeably. If you’re really interested, there is an ongoing debate on Quora about coding versus programming . What I am more interested in as a STEM educator is computational thinking.

So What’s Computational Thinking?

According to Carnegie Mellon Centre for Computational Thinking , “computational thinking is a way of solving problems, designing systems, and understanding human behaviour that draws on concepts fundamental to computer science.”

Jeanette M. Wing wrote a seminal piece on computational thinking in 2006 and prefaced the essay with this line: “It (computational thinking) represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.”

The rest of this blog post is strongly influenced by Wing’s piece in 2006 and lifts quotes from the article, the most important of which is the following line.

“To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability.” Clearly Wing believes computational thinking is linked to skills beyond the ability to write code. Her belief is that “computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science.”

At Saturday Kids , our belief is that problem solving starts with the user (to Wing’s point, ‘understanding human behavior’) and understanding user needs. That is why we are huge advocates of the human-centered approach of design thinking. Understanding user needs is Step 0 (in computer science numbers start with zero instead of one), knowing what problem to solve for is Step 1.

Where computational thinking comes in are the steps after you know what problem it is you’re solving for. As Wing puts it, “computational thinking is reformulating a seemingly difficult problem into one we know how to solve, perhaps by reduction, embedding, transformation, or simulation.”

In Saturday Kids classes , we ask students to break a problem down into smaller problems (e.g to build an animation in Scratch , start with one sprite, make the sprite do something, move on to the next sprite when you’re happy with the first one). The ScratchEd team calls this ‘experimenting and iterating.’

Whatever you want to call it, this much is clear: Computational thinking is not about writing code machines can understand. It is about solving problems efficiently. Wing listed down a number of characteristics of computational thinking. I leave you with the most important. “Fundamental, not rote skill. A fundamental skill is something every human being must know to function in modern society.”

More stories you may enjoy

Decision Making vs. Problem Solving

What's the difference.

Decision making and problem solving are two closely related concepts that are essential in both personal and professional settings. While decision making refers to the process of selecting the best course of action among various alternatives, problem solving involves identifying and resolving issues or obstacles that hinder progress towards a desired outcome. Decision making often involves evaluating different options based on their potential outcomes and consequences, while problem solving requires analyzing the root causes of a problem and developing effective strategies to overcome it. Both skills require critical thinking, creativity, and the ability to weigh pros and cons. Ultimately, decision making and problem solving are interconnected and complementary processes that enable individuals to navigate complex situations and achieve desired goals.

Further Detail

Introduction.

Decision making and problem solving are two essential cognitive processes that individuals and organizations engage in to navigate through various challenges and achieve desired outcomes. While they are distinct processes, decision making and problem solving share several attributes and are often interconnected. In this article, we will explore the similarities and differences between decision making and problem solving, highlighting their key attributes and how they contribute to effective problem-solving and decision-making processes.

Definition and Purpose

Decision making involves selecting a course of action from multiple alternatives based on available information, preferences, and goals. It is a cognitive process that individuals use to make choices and reach conclusions. On the other hand, problem solving refers to the process of finding solutions to specific issues or challenges. It involves identifying, analyzing, and resolving problems to achieve desired outcomes.

Both decision making and problem solving share the purpose of achieving a desired outcome or resolving a particular situation. They require individuals to think critically, evaluate options, and consider potential consequences. While decision making focuses on choosing the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges.

Attributes of Decision Making

Decision making involves several key attributes that contribute to its effectiveness:

• Rationality: Decision making is often based on rational thinking, where individuals evaluate available information, weigh pros and cons, and make logical choices.
• Subjectivity: Decision making is influenced by personal preferences, values, and biases. Individuals may prioritize certain factors or options based on their subjective judgment.
• Uncertainty: Many decisions are made under conditions of uncertainty, where individuals lack complete information or face unpredictable outcomes. Decision makers must assess risks and make informed judgments.
• Time Constraints: Decision making often occurs within time constraints, requiring individuals to make choices efficiently and effectively.
• Trade-offs: Decision making involves considering trade-offs between different options, as individuals must prioritize certain factors or outcomes over others.

Attributes of Problem Solving

Problem solving also encompasses several key attributes that contribute to its effectiveness:

• Analytical Thinking: Problem solving requires individuals to analyze and break down complex problems into smaller components, facilitating a deeper understanding of the issue at hand.
• Creativity: Effective problem solving often involves thinking outside the box and generating innovative solutions. It requires individuals to explore alternative perspectives and consider unconventional approaches.
• Collaboration: Problem solving can benefit from collaboration and teamwork, as diverse perspectives and expertise can contribute to more comprehensive and effective solutions.
• Iterative Process: Problem solving is often an iterative process, where individuals continuously evaluate and refine their solutions based on feedback and new information.
• Implementation: Problem solving is not complete without implementing the chosen solution. Individuals must take action and monitor the outcomes to ensure the problem is effectively resolved.

Interconnection and Overlap

While decision making and problem solving are distinct processes, they are interconnected and often overlap. Decision making is frequently a part of the problem-solving process, as individuals must make choices and select the most appropriate solution to address a specific problem. Similarly, problem solving is inherent in decision making, as individuals must identify and analyze problems or challenges before making informed choices.

Moreover, both decision making and problem solving require critical thinking skills, the ability to evaluate information, and the consideration of potential consequences. They both involve a systematic approach to gather and analyze relevant data, explore alternatives, and assess the potential risks and benefits of different options.

Decision making and problem solving are fundamental cognitive processes that individuals and organizations engage in to navigate through challenges and achieve desired outcomes. While decision making focuses on selecting the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges. Both processes share attributes such as rationality, subjectivity, uncertainty, time constraints, and trade-offs (in decision making), as well as analytical thinking, creativity, collaboration, iterative process, and implementation (in problem solving).

Understanding the similarities and differences between decision making and problem solving can enhance our ability to approach complex situations effectively. By leveraging the attributes of both processes, individuals and organizations can make informed choices, address challenges, and achieve desired outcomes.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Written by Vish Khanna

Unstructured Data Management Platform » Data Management » How to Find Your Path: Choosing Between Data Science and Data Analytics

How to Find Your Path: Choosing Between Data Science and Data Analytics

What is data science, what is data analytics, comparing data science vs. data analytics, faqs about data science vs. data analytics, the right discipline for your needs.

The terms “data science” and “data analytics” are often used interchangeably, but they represent distinct fields with different goals, processes, and skill sets. Understanding the differences between these two disciplines is crucial for professionals who work with data, as well as for organizations looking to leverage data for strategic advantage. 

This article will explore the nuances of data science vs. data analytics, compare their roles and processes, and help you determine which path might be right for you or your organization.

Data science is a multidisciplinary field that combines various techniques from statistics, computer science, and domain knowledge to extract insights and knowledge from data. 

As data becomes increasingly central to decision-making, data science plays a crucial role in helping you understand patterns, make predictions, and drive informed business strategies. It’s not just about analyzing data but also about using advanced tools and algorithms to create statistical models that can predict future outcomes and uncover hidden trends.

Data Science Process

The data science process is a structured approach that guides you through solving complex problems with data. Here’s how it typically unfolds:

• Define the Problem: Start by clearly defining the question you need to answer or the problem you need to solve.
• Collect Data: Gather the necessary data from various sources. This could include structured data from databases, unstructured data like text or images, and even real-time data from sensors.
• Data Cleaning: Cleaning data involves removing duplicates, handling missing values, and correcting errors to ensure the data is accurate and ready for analysis.
• Exploratory Data Analysis (EDA): Explore the data to identify patterns, trends, and relationships. EDA helps you understand the data’s structure and the variables that might influence the outcome.
• Modeling: Build predictive models using techniques like machine learning or statistical analysis. These models help you make decisions based on the data.
• Validation: Test the model to ensure it accurately predicts outcomes on new data. This step is crucial for confirming the reliability of your model.
• Deployment and Monitoring: Implement the model in a production environment where it can be used to make decisions. Continuous monitoring is needed to ensure the model remains accurate over time.

Data Scientist Role

As a data scientist, your role is to transform raw data into actionable insights. You do this by applying advanced analytical techniques, developing predictive models, and communicating findings in a way that’s accessible to non-technical stakeholders. 

The role is highly collaborative, often requiring you to work with data engineers, business analysts, and decision-makers to ensure that the insights you generate align with organizational or career goals.

Data Scientist Skills

To excel as a data scientist, you need a combination of technical and analytical skills:

• Programming Skills: Proficiency in programming languages like Python, R, and SQL is essential for data manipulation, analysis, and modeling.
• Statistics and Mathematics: A strong foundation in statistics and mathematics is crucial for building models and interpreting data.
• Machine Learning: Understanding and applying machine learning algorithms allows you to create predictive models that can automatically improve over time.
• Data Wrangling : The ability to clean, transform, and merge datasets from different sources is key to preparing data for analysis.
• Data Visualization Software: Skills in tools like Tableau, Power BI, or Matplotlib help you present data in a clear and visually compelling way.
• Domain Knowledge: Understanding the industry or field you’re working in allows you to apply data science techniques in a relevant and impactful way.
• Communication: The ability to explain complex data science concepts and findings to non-technical stakeholders is vital for ensuring that your insights lead to informed decisions.

A Day in the Life of a Data Scientist

A typical day for a data scientist involves a mix of analysis, collaboration, and problem-solving.

Morning: You start the day by checking the performance of your deployed models. This might involve analyzing logs, reviewing accuracy metrics, and addressing any issues that arise. Next, you attend a team meeting where you discuss ongoing projects, share insights, and align on priorities with data engineers and business stakeholders.

Midday: You dive into data exploration, perhaps working on a new dataset that has just been provided. Using tools like Python and Jupyter Notebooks, you perform exploratory data analysis to identify trends or anomalies. You might also spend time cleaning and transforming the data to prepare it for modeling.

Afternoon: After lunch, you focus on building or refining a predictive model. This involves selecting the right algorithms, tuning hyperparameters, and validating the model’s performance. Once satisfied, you may present your findings to stakeholders, using visualization tools to make complex insights understandable.

End of Day: The day wraps up with documentation and planning. You document your work, ensuring that your code and analysis are reproducible. You also plan for the next day, setting goals and identifying any data or resources you’ll need to move forward.

In this role, you continually balance technical tasks with strategic thinking, making data science a dynamic and rewarding field.

Data analytics is the process of examining existing data sets to draw conclusions and make informed decisions. It involves using statistical techniques, algorithms, and tools to interpret data and uncover patterns, correlations, and trends. 

While data science focuses on creating models and making predictions, data analytics is more about answering specific questions and making data-driven decisions based on historical data. 

Data analytics is a critical function in any organization that relies on data to optimize processes, understand customer behavior, or improve product development and services.

Data Analytics Process

The data analytics process is systematic and involves several key steps that help you extract meaningful insights from data. Here’s an overview of how it typically works:

• Data Collection: The first step is to gather data from various sources such as databases, surveys, sensors, or other data repositories. The quality of your analysis depends heavily on the accuracy and relevance of the data collected.
• Data Cleaning: Before analysis, you must clean the data to remove any errors, duplicates, or inconsistencies. This step ensures that your analysis is based on reliable and accurate data.
• Data Analysis: Once the data is cleaned, you apply statistical techniques and tools to analyze the data. This could involve anything from simple descriptive statistics to complex algorithms, depending on the goals of the analysis.
• Data Interpretation: After analysis, you interpret the results to draw conclusions. This might involve identifying trends, correlations, or outliers that can inform business decisions.
• Data Visualization: To communicate your findings, you create visualizations that make the data easy to understand. Tools like charts, graphs, and dashboards help convey insights to stakeholders in a clear and actionable way.
• Reporting: The final step is to compile your findings into a report or presentation, highlighting key insights and recommendations. This helps decision-makers understand the implications of the data and take appropriate action.

The Types of Data Analytics

Data analytics can be divided into four main types, each serving a different purpose in understanding and utilizing data.

Predictive Analytics

Predictive analytics involves using historical data to make predictions about future outcomes. By analyzing patterns and trends, you can forecast events such as customer behavior, market trends, or potential risks. Common applications include sales forecasting, risk management, and customer churn prediction.

Prescriptive Analytics

Prescriptive analytics goes a step further by not only predicting future outcomes but also recommending actions to achieve desired results. It uses optimization techniques to suggest the best course of action in a given situation. This type of analytics is often used in supply chain management, pricing strategies, and resource allocation.

Diagnostic Analytics

Diagnostic analytics helps you understand why something happened. By examining data from multiple sources and identifying correlations, you can uncover the root causes of past events. It’s commonly used in quality control, financial analysis, and customer feedback analysis to determine the reasons behind certain outcomes.

Descriptive Analytics

Descriptive analytics provides an overview of what has happened in the past. It’s about summarizing historical data to identify patterns or trends that can inform future decisions. This is often used in reporting, performance metrics, and market analysis to understand past performance and trends.

Data Analyst Role

As a data analyst, your primary role is to examine data and draw actionable insights that help drive business decisions. You’re responsible for collecting, processing, and analyzing data to identify trends or patterns that can impact the organization. You often work closely with business leaders, marketers, and other stakeholders to ensure that the insights you provide are aligned with the company’s objectives.

Data Analyst Skills

To succeed as a data analyst, you need a blend of technical skills and analytical skills, including:

• Data Analysis: Proficiency in statistical analysis and the ability to use tools like Excel, R, or Python to interpret data.
• Data Management: Knowledge of databases and data management systems such as SQL to efficiently store and retrieve data.
• Data Visualization: Skills in creating visual representations of data using tools like Tableau, Power BI, or Excel.
• Critical Thinking: The ability to critically assess data, identify trends, and understand the implications of your findings.
• Attention to Detail: Ensuring data accuracy and reliability is crucial, so attention to detail is key.
• Communication: The ability to translate complex data insights into clear, actionable recommendations for non-technical stakeholders.

A Day in the Life of a Data Analyst

A typical day for a data analyst involves a mix of data exploration, reporting, and collaboration. Here’s what your day might look like:

Morning: You start the day by reviewing data from various sources, checking for any discrepancies, and preparing datasets for analysis. You might also attend a team meeting to discuss ongoing projects and align on priorities with business stakeholders.

Midday: You dive into data analysis, using tools like Excel or SQL to explore datasets and identify trends. You might be working on a report that highlights key metrics for a marketing campaign or analyzing sales data to provide insights into customer behavior.

Afternoon: After lunch, you focus on creating visualizations and compiling your findings into a report. You use tools like Tableau or Power BI to create dashboards that make the data easy to understand. You might also present your findings to stakeholders, discussing the implications and recommendations.

End of Day: The day ends with documentation and planning. You document your analysis, ensuring that your work is reproducible and accessible to others. You also plan for the next day, setting goals and preparing any data or resources you’ll need.

In this role, you continually work to uncover insights from data, making it a critical function in helping organizations make informed decisions.

Data science and data analytics are closely related fields, but they differ in scope, techniques, and objectives. Below is a comparison that highlights the key differences and similarities.

Here are some common questions people ask about data science vs. data analytics. 

Which is better: data analyst or data scientist?

Neither role is better as they serve different purposes. A data scientist focuses on predictive modeling and strategic insights, while a data analyst interprets data for specific questions. Choose based on your interests—problem-solving with advanced models (data science) or analyzing existing data for decisions (data analytics).

Can a data analyst become a data scientist?

Yes, a data analyst can transition to a data scientist by learning advanced technical skills such as machine learning, programming, and statistical modeling (the soft skills carry over). Building expertise in Python, R, and big data tools is essential. Many data scientists start as data analysts and upskill over time.

Do data analysts make more than data scientists?

Typically, data scientists earn more than data analysts due to the advanced skills required and the strategic nature of their role. Salaries vary based on experience, industry, and location, but the average salary for data science roles is generally higher.

Will AI replace data analysts?

Artificial intelligence works as a copilot for data analysts, but it won’t fully replace them. Analysts will still be needed to interpret AI-generated insights, ensure data quality, and make informed decisions based on the context that AI might miss. Furthermore, AI can’t be held responsible for its output, so oversight is necessary. 

What should I learn first: data analytics or data science?

Start with data analytics if you’re new to the field. It provides a foundation in data handling, statistical analysis, and basic tools like Excel and SQL. Once comfortable, you can advance to data science, which involves more complex techniques like machine learning and predictive modeling.

Is data science harder than data analytics?

Data science is generally more complex because it involves advanced programming, machine learning, and predictive modeling. Data analytics focuses on interpreting existing data, which requires a strong understanding of statistics and visualization but is typically less technically demanding than data science.

Which career trajectory has more opportunities: data science or data analytics?

Both fields offer a strong career path, but data science tends to have a broader scope and more varied roles, particularly in industries like tech, finance, and healthcare. Data analytics is in demand across all sectors, especially in business intelligence and operations.

How long does it take to become a data scientist?

Becoming a data scientist can take many years of education and experience. A strong foundation in mathematics, statistics, and programming is essential, often requiring an advanced degree in a related field plus additional training or work experience in data analysis and machine learning.

Data science and data analytics are both essential to extracting value from data, but they serve different purposes and require different skills. Whether you’re interested in predicting future trends with advanced models or analyzing existing data to drive decisions, understanding the distinctions between these fields will help you make informed choices in your career trajectory or business strategy.

Read more from Shelf

Written By Andrew Batutin

What is a Data Mesh?

A data mesh is a modern approach to data architecture that decentralizes data ownership and management, thus allowing domain-specific teams to handle their own data products. This shift is a critical one for organizations dealing with complex, large-scale data environments – it can enhance...

Written By Shelf

7 Types of Knowledge Management Tools

The list of knowledge management tools ideally designed to help an organization find and manage helpful information can be overwhelming. You probably can think of a handful of active software subscriptions in your company with some kind knowledge management component; these tools may be siloed...

Knowledge Management vs Information Management: Key Differences

Many organizations are exploring knowledge management because a proper knowledge management system is integral to a successful artificial intelligence strategy. If your organization has hundreds of files or decades of stored data and your own workforce can’t navigate it, then an artificial intelligence (or large language model) won’t be able to either.

• DSA Tutorial
• Data Structures
• Linked List
• Dynamic Programming
• Binary Tree
• Binary Search Tree
• Divide & Conquer
• Mathematical
• Backtracking
• Branch and Bound
• Pattern Searching

Difference between Recursion and Iteration

A program is called recursive when an entity calls itself. A program is called iterative when there is a loop (or repetition).

Example: Program to find the factorial of a number 

Time and Space Complexity

Below is a detailed explanation to illustrate the difference between the two using the above example. We will study the different aspects of both recursive and iterative approaches.

1. Time Complexity

The time complexity of the method may vary depending on whether the algorithm is implemented using recursion or iteration.

• Recursion : The time complexity of recursion can be found by finding the value of the nth recursive call in terms of the previous calls. Thus, finding the destination case in terms of the base case, and solving in terms of the base case gives us an idea of the time complexity of recursive equations. Please see Solving Recurrences for more details.  
• Iteration : The time complexity of iteration can be found by finding the number of cycles being repeated inside the loop. 

Usage of either of these techniques is a trade-off between time complexity and size of code. If time complexity is the point of focus, and the number of recursive calls would be large, it is better to use iteration. However, if time complexity is not an issue and shortness of code is, recursion would be the way to go.

• Recursion : Recursion involves calling the same function again, and hence, has a very small length of code. However, as we saw in the analysis, the time complexity of recursion can get to be exponential when there are a considerable number of recursive calls. Hence, usage of recursion is advantageous in shorter code, but higher time complexity.  
• Iteration : Iteration is the repetition of a block of code. This involves a larger size of code, but the time complexity is generally lesser than it is for recursion. 

3. Overhead

Recursion has a large amount of Overhead as compared to Iteration. 

• Recursion : Recursion has the overhead of repeated function calls, that is due to the repetitive calling of the same function, the time complexity of the code increases manyfold.  
• Iteration : Iteration does not involve any such overhead. 

4. Infinite Repetition

Infinite Repetition in recursion can lead to a CPU crash but in iteration, it will stop when memory is exhausted. 

• Recursion : In Recursion, Infinite recursive calls may occur due to some mistake in specifying the base condition, which on never becoming false, keeps calling the function, which may lead to a system CPU crash.  
• Iteration : Infinite iteration due to a mistake in iterator assignment or increment, or in the terminating condition, will lead to infinite loops, which may or may not lead to system errors, but will surely stop program execution any further.

Difference between Iteration and Recursion

The following table lists the major differences between iteration and recursion:

Please Login to comment...

Similar reads.

• Technical Scripter
• Algorithms-Recursion
• Technical Scripter 2018
• How to Get a Free SSL Certificate
• Best SSL Certificates Provider in India
• Elon Musk's xAI releases Grok-2 AI assistant
• What is OpenAI SearchGPT? How it works and How to Get it?
• Content Improvement League 2024: From Good To A Great Article

Improve your Coding Skills with Practice

What kind of Experience do you want to share?