how to learn problem solving coding

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

Codility Tests

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Technical Screening Tools

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

Sharifa Ismail Yusuf

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

dewayne sewell

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

Pakize Bozkurt

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

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

Ipsa iure sed rerum

Excepturi quo volupt

Thanks for this !

Fahil kiima

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

Alan Codinho

Nice overview

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The Beginner Programmer's guide to Problem Solving [With Example]

Have you got this feeling that you are able to grasp the concepts of programming and  you are able to understand what’s a variable, what’s a function, what are data types, etc. yet you find it difficult to solve problems in programming.  Every beginner gets this feeling.  I did too when starting out.

It is important to overcome this feeling at the earliest, otherwise it can form a mental block for you.

How it can be a mental block to you?  Common sense says that the more you practice a certain skill, you get better at that skill as time progresses.  Same goes with problem solving too.  The more problems you solve, the better you become at problem solving.  But when you get a feel that you are trying hard and still unable to solve a problem or find it extremely difficult, your confidence lowers.  At this stage, either you stop solving problems or try to solve lesser number of problems.

The point is your curriculum or your professional work is generally designed in such a manner that the order of difficulty increases as time progresses.  So, you are in a situation where you feel less confident in solving small problems but now tasked with solving bigger problems.  And the cycle continues till it becomes a permanent mental block in you.

Is it too late to start solving problems?

No.  If you have come to the realization that you need to improve your problem solving skills, you have made that good first step.  Quite often our egos don’t let us accept the obvious.  It is good to accept certain truth because that is the only way that we can improve ourselves.

What can I do to become better at solving problems?

Remove the mental block first – exercise your mind.

Your mind is your most powerful weapon.  So you have to think you can actually solve the problem.  So from today, think positively that you can solve any problem.  But you will obviously start with small problems and go on to solve bigger problems.

As with every aspect in life, it starts with conditioning the mind.  So, starting today, tell yourselves the following:

• I can solve any problem that is put at me
• I will commit at least 1-2 hours per day on solving problems alone for the next 30 days
• I will never give up on any problem that is put at me, I will ask for help if required.1

Understand the basic approach to problem solving

Do you know one of the reasons for your struggle with problem solving?  One reason might be due to lack of practice.  But the main reason is because you have not understood the basics of problem solving especially in programming.  Once you understand the approach to problem solving to the smallest of things, you can go ahead and solve bigger and more complex problems with confidence.1

Ever wondered how top tech companies like Google, Amazon solved the internet’s biggest & hardest problems?  The answer is simplicity.  They solved problems at the basic level and then went on to solve bigger and bigger problems.  You can do it too.  But you need to be good at the basics.

What do I need to understand before even trying to solve the problem?

Understand the problem clearly – the power of clarity.

You need to understand your problem clearly before even trying to solve it1.  Lack of clarity at this stage will put you down.  So make a conscious effort in understanding the problem more clearly.  Ask questions like What, Why, When, Where, What if and How.  Not all questions might be applicable to your problem, but it is important to ask questions to yourself at this stage before you go ahead trying to solve the problem.

Visualize – The Power of visualization

I am sure everyone of you is aware of what visualization is.  Trying to picturize your thoughts.  Have you ever imagined how some people can solve extra ordinary problems just by looking into those problems and they will instantly have a solution to it?  And we don’t even understand the problem fully?  It is because they do it with their mind.  They visualize the problem in their minds and they solve it in their minds itself.  Visualization is a powerful tool in your mind.

But in order to get to that state, first you need to visualize the problem externally.  That is where a pen and a paper/notebook (or) a white board comes into play1.  Try to visualize the problem at hand and try to picturize the problem.  That is also one of the steps to make sure that you understand the problem clearly.

There was a situation when I and my dear friend & colleague were discussing about a problem and we were literally going nowhere.  This was actually when we each had around 7 years of experience in the industry.  At that point, my friend said “Let’s put our points in board.  If we don’t put it on the board, we will never get started”.  And we started putting things on board.  Things started to get more clear and raised more questions and ultimately became more clear.

That is the power of visualization.  It really helps us to get started with our thinking. This visual thing works.  Just try it out.

Your next question might be “I kinda get it, but I don’t.  How do I visualize? What exactly do I visualize?”.  Please read on to find out the answers.

What is the basic approach to problem solving

Step 1:  identify small problems.

The major trick in problem solving is to identify and solve the smallest problem and then moving ahead with bigger ones.  So how do you do it?

The answer is division of responsibility.  Simply put, we need to identify parts that can stand on its own and identify a sequence in those responsibilities.  And once you start breaking down the problems into smaller ones, then you can go ahead with the next step.

Step 2:  Solve the smaller problems one at a time

Now that you have identified the smaller problems, try to solve them.  While solving them, make sure that you are focussing only on one problem at a time.  That makes life much simpler for us.  If you feel that this smaller problem is too big to solve on its own, try to break it down further.  You need to iterate steps 1 to step 3 for each smaller problem.  But for now, ignore the bigger problem and solve the rest of the problems.

• It is ok to assume that other problems are solved
• It is ok to hardcode when coding a particular problem, but later you will resolve it in step 3.
• Solve the easier problems first, that will give you confidence and momentum until you get the confidence to solve the hardest problem first.

Step 3: Connect the dots (Integration)

You have solved individual problems.  Now it is time to connect the dots by connecting the individual solution.  Identify those steps which will make the solution or the program complete.  Typically in programming, the dots are connected by passing data that is stored in variables.

Step 4: Try to optimize each step & across steps

Once you are completed with a working solution, try to optimize the solution with the best code that you can write.  This comes only with practice.  This trick can make a difference between a good programmer and a great programmer.  But to get to this step, you need to be first good at steps 1 to 3.

Let’s take an example & walkthrough the problem solving approach

Problem:  check if a user given string is a palindrome or not.

I will be using Python for this exercise (Although I have experience in1 C# and JAVA, I am also a Python beginner, so pardon any bad code).  Let’s iterate through our steps:

Let’s call this as Level 1:

Step 1:  Identify smaller problems:

Step 2: Solve the small problems

So each small problem will map to its corresponding solution as below:

Note: When solving the step (3.  Compare the variables), I am doing 2 things:

• I am making an assumption that reversed is the variable name of the reversed string.
• I am hardcoding the variable name reversed to ‘madam’ to avoid compile time error
• If you execute the program at this state, you can input ‘madam’ and check if it is printing ‘The given string is a palindrome’ (And) you can input something else like ‘dog’ and check if it is printing ‘The given string is not a palindrome’

When we are trying to connect the dots, the only thing that is missing now is the variable reversed is hardcoded.  For that to be set to the correct value, we need to break the small problem (Reverse the user input and store in a separate variable) into further smaller problems.  Till that point we need to mark it as incomplete.

2 things still remain unsolved in Level 1:

• Solution for step 2 in the diagram (Reverse the user input and store in a separate variable)
• Connecting the dots once the solution for step 2 is found

Iterating small problem 2 through our problem solving steps:

Let’s call this Level 2:

Step 1: Identify smaller problems

Step 3: Connect the dots

Here, we have already connected the dots.  So we need not do anything extra in this step.

Now we have solved the smaller problems, which means Level 2 is over.  Now we need to come back to Level 1.

If you remember, 2 things remain in Level 1.  One is solution for step 2 which we have found now.  Two is connecting the dots.

Now if we substitute the small problem 2 with the solution that we derived just now, we get something like this:

The thing that remains is connecting the dots.

So if we see what is the missing connection, the variable reversed is set twice.  One to the solution of step 2 and another is hardcoded in step 3.  So we can now remove the hardcoded value in step 3, in which case our code will become like this

If you see, we have actually solved our problem.

We are left with step 4 – Optimize each step and across steps

Step 4: Try to optimize each step and across steps

As you can see, there are many things that needs to be optimized for this code.  I would leave you to optimize the code further.  Come on, put on your thinking cap and try different solutions.

BONUS STEP 5:  Make the code robust

By robust I mean,

• Adding error & exception handling
• Using better variable names
• Adding user defined functions
• Adding comments where necessary

Again, I would leave you to figure out how to do this step.

• We saw just how we can solve problems using a step by step approach
• By solving smaller problems, I get into a momentum for solving bigger & tougher problems
• By focussing one problem at a time, I am eliminating distractions, thus allowing to better direct your efforts for that one problem rather than getting confused with many small problems at hand.
• If you understand this approach and practice, you will definitely go on to solve bigger problems and your confidence will raise.
• Beauty about breaking down the problem is that we can further convert each problem and sub problem into separate functions/modules thus making the code more modularized and maintainable.

Wait, You can’t leave yet:

Now dear beginner programmers, take any problem and try to apply this approach.  See the results for yourselves.  Now, describe the following in the comments section:

• What problem you are solving?
• How did you break it down? (Even a snap of your notebook page or board will do!)
• The final code
• How did you feel and what did you learn from this exercise?

Also remember, I am challenging you for the 30 day problem solving challenge.

If you liked this blog post, please feel free to share it with your circles in social media.

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

Comments and Discussions

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Anyone can learn to code these days. Here’s 4 pieces of advice beginners need to know

Most of the world’s top billionaires will tell you that learning how to code is a skill that will only help you. In fact, Bill Gates has said that truly everyone can benefit from learning the basics of computer science.

If you’re one of those who thought it would be fun—and useful—to learn how to code, you’re in luck. Now more than ever before, it’s easier for those of every age to learn coding.

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In fact, there’s almost too many different ways to learn. For instance, instead of having to decipher through a boring textbook, you can learn to code alongside your favorite Disney character or as you explore Mars. These are just some of the gamified ways through platforms like Hour of Code .

Ori Bendet, vice president of product management at Checkmarx, says learning coding languages is just as important as students learning a second spoken language. He started learning when he was in elementary school, and now he’s helping his 8-year-old son do the same.

“What matters is how you can think about problem solving in a logical way,” he says. “And this is why I believe it’s so important to teach that to kids at a young age.”

Start out simple—and fun

The best way to get started with coding is to jump right in. On YouTube , there are an endless number of videos focused on coding tutorials. Start by exploring and find a content creator who sparks your interest. Follow alongside their work in your own code editor. ( W3Schools has a free online frontend and backend editor for you to play around with.)

You should also check out Hour of Code’s hundreds of free coding games (yes, some are even fun for adults). They partnered with popular companies and characters to make learning coding basics enjoyable. For example, you can code snowflakes with Anna and Elsa from Frozen, create a Mars exploration game with NASA, or even program a new Star Wars droid. 

And some of the biggest video games on the market like Roblox , Fortnite, and Minecraft all have created ways to learn coding while playing. 

Keep it up; practice makes perfect

Just like any foreign language, you can only get better with practice and immersion. So, if you’re serious about learning, dedicate a set amount of time each day or week to exploring videos, playing games, and building out your own projects. 

Persistence and a growth mindset are ultimately the keys to learning the basics of coding, says Pat Yongpradit, chief academic officer, at Code.org.

“…unlike other skills, coding is marked by constant feedback where you never get what you want the first—or seventeenth time—and you will have never-ending opportunities to revise your work,” he tells Fortune . 

As you improve, you can also see camaraderie with others who are in your same boat. The subReddit r/learnprogramming is one of the biggest communities on Reddit. It can be a great place to hear others’ ideas and frustrations—and to voice your own.

Build a portfolio showcasing your skills

If you want to be able to prove to others—and to yourself—that you know your stuff, you need something to show for it. As you begin to explore the creation of new projects—whether it’s as simple as a tic-tac-toe game, random number generator, or responsive website feature—make sure you save them on a repository like GitHub. 

This is a good practice to develop because down the line when the time comes to explore internships or even jobs, you are able to easily show off your skills.

Two other added benefits are that you can go back and see how much progress you’ve made over the years as well as be able to share your projects with others, so they can learn, too.

Recognize the evolving coding ecosystem

There’s no question that the world of coding is changing. Artificial intelligence (AI) is completely changing the game when it comes to workflow. Already, the technology is able to program basic projects by itself and help identify bugs. In fact, you can ask ChatGPT for code for a certain project, such as a Chess game, and it will provide it in Python or another language.

While AI is still not perfect, it is only getting better—and rapidly. However, some experts still place doubt that it will replace programmers any time soon.

Benedet compares the evolution of AI with coding to the shift from manual to automatic cars—while the automatic car does more of the work, you still have to learn how to drive. Similarly, tech experts still need to learn how to code.

“Developers will spend less time on the logistics of code or software, and allowing them more time to code and solve business problems using code,” he says. “And this is, I think, what AI will bring to the table.” 

Others, like Kevin Kelly, director of the AWS Cloud Institute , thinks AI and prompt engineering will replace some of the higher level programming languages like Java, Python, and C# in as little as 2–5 years.

“Prompt engineering and the use of large language conversational models is going to democratize the ability for lots more people to become prompt writers,” he says.

This is why AWS’s cloud developer program—which trains individuals with little to no experience to be job ready in as little as one year—focuses so heavily on teaching learners how to be effective prompt coders.

“I think if you’re not paying attention to gen AI at the moment, you do yourself a disservice,” Kelly adds.

Can I teach myself to code?

Absolutely. A lot of people are teaching themselves how to code every single day. Coding is a lot of trial and error, so after watching some self-paced videos or lessons, start experimenting with what you learn.

Otherwise, there is certainly a growing emphasis on computer science education in K-12 settings. And there are a number of opportunities in community college and beyond.

Which coding program is best for beginners?

You might get a different answer for every person you ask. There are dozens of coding learning platforms like Codecademy and Scratch that offer basic walkthroughs. 

In terms of languages, if you like web development, then you can’t go wrong with understanding HTML and CSS. Otherwise Python, JavaScript, and Scratch are three of the best programming languages to learn . Above all, no matter where you start, you’re learning the soft skills of problem solving and critical thinking—which can be translated through the world of computer science.

Is coding easy to learn?

Rome wasn’t built in a day. Neither will be your coding skills. But, if you put in the proper time and dedication—as well as have realistic expectations—you can be on track to be a master coder within years. 

Certainly, it is easier to learn how to code than a decade ago, and ultimately, the sky’s the limit. You can just as easily build the next big product or game.

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Programming Tutorial | Introduction, Basic Concepts, Getting started, Problems

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

Programming Tutorial

1. What is Programming?

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

Table of Content

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

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

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

A. How Programming Works:

Programming involves several key steps:

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

B. Benefits of Learning to Code:

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

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

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

2. Getting Started with Programming Tutorial

A. choosing your first language.

Assess Resource Availability:

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

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

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

C. Setting Up Your Development Environment

Choose a Text Editor or IDE :

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

Install a Compiler or Interpreter:

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

Download Additional Software (if needed):

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

Test Your Environment:

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

3. Common Programming Mistakes and How to Avoid Them

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

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

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

A. Variables and Data Types:

Understanding Variable Declaration and Usage:

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

Exploring Different Data Types:

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

Operations with Different Data Types:

Each data type has supported operations.

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

B. Operators and Expressions:

Arithmetic Operators:

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

Comparison Operators:

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

Logical Operators: Combine conditions and produce True or False.

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

Building Expressions:

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

C. Control Flow Statements:

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

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

Looping Statements: Repeat a block of code multiple times.

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

Nested Loops and Conditional Statements:

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

D. Functions:

Defining and Calling Functions:

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

Passing Arguments to Functions:

• Values passed to functions for processing.

Returning Values from Functions:

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

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

5. Advanced Programming Concepts

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

A. Object-Oriented Programming (OOP)

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

1. Classes and Objects:

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

2. Inheritance and Polymorphism:

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

3. Encapsulation and Abstraction:

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

B. Concurrency and Parallelism

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

1. Multithreading and Multiprocessing:

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

2. Synchronization and Concurrency Control:

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

6. Writing Your First Code

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

Printing “Hello world” in C++:

Explanation of above C++ code:

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

Printing “Hello world” in Java:

Explanation of above Java code:

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

Printing “Hello world” in Python:

Explanation of above Python code:

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

Printing “Hello world” in Javascript:

Explanation of above Javascript code:

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

Printing “Hello world” in PHP:

Explanation of above PHP code:

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

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

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

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

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

A. Deepen your understanding of Basic Programming Concepts:

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

B. Learn advanced concepts:

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

C. Choose a focus area:

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

D. Build projects:

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

9. Resources and Further Learning

A. Online Courses and Tutorials:

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

B. Books and eBooks:

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

C. Programming Communities and Forums:

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

D. Tips for Staying Motivated and Learning Effectively:

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

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

Question 1: how to learn programming without tutorial.

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

Question 2: How to learn coding tutorial?

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

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

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

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

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

Question 5: Can tutorials help coding?

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

Question 6: How do you use tutorials effectively?

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

Question 7: Can coding be done on a phone?

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

Question 8: Can I learn coding on GeeksforGeeks?

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

Question 9: Can we do coding on a laptop?

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

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

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

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

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Exploring generative ai at harvard.

Jessica McCann

Harvard Correspondent

Leaders weigh in on where we are and what’s next

The explosion of generative AI technology over the past year and a half is raising big questions about how these tools will impact higher education. Across Harvard, members of the community have been exploring how GenAI will change the ways we teach, learn, research, and work.

As part of this effort, the Office of the Provost has convened three working groups . They will discuss questions, share innovations, and evolve guidance and community resources. They are:

• The Teaching and Learning Group , chaired by Bharat Anand , vice provost for advances in learning and the Henry R. Byers Professor of Business Administration at Harvard Business School. This group seeks to share resources, identify emerging best practices, guide policies, and support the development of tools to address common challenges among faculty and students.
• The Research and Scholarship Group , chaired by John Shaw , vice provost for research, Harry C. Dudley Professor of Structural and Economic Geology in the Earth and Planetary Sciences Department, and professor of environmental science and engineering in the Paulson School of Engineering and Applied Science. It focuses on how to enable, and support the integrity of, scholarly activities with generative AI tools.
• T he Administration and Operations Group , chaired by Klara Jelinkova , vice president and University chief information officer. It is charged with addressing information security, data privacy, procurement, and administration and organizational efficiencies.

Klara Jelinkova, Bharat Anand, and John Shaw.

Photos by Kris Snibbe/Harvard Staff Photographer; Evgenia Eliseeva; and courtesy of John Shaw

The Gazette spoke with Anand, Shaw, and Jelinkova to understand more about the work of these groups and what’s next in generative AI at Harvard.

When generative AI tools first emerged, we saw universities respond in a variety of ways — from encouraging experimentation to prohibiting their use. What was Harvard’s overall approach?

Shaw: From the outset, Harvard has embraced the prospective benefits that GenAI offers to teaching, research, and administration across the University, while being mindful of the potential pitfalls. As a University, our mission is to help enable discovery and innovation, so we had a mandate to actively engage. We set some initial, broad policies that helped guide us, and have worked directly with groups across the institution to provide tools and resources to inspire exploration.

Jelinkova: The rapid emergence of these tools meant the University needed to react quickly, to provide both tools for innovation and experimentation and guidelines to ensure their responsible use. We rapidly built an AI Sandbox to enable faculty, students, and staff to experiment with multiple large language models in a secure environment. We also worked with external vendors to acquire enterprise licenses for a variety of tools to meet many different use cases. Through working groups, we were able to learn, aggregate and collate use cases for AI in teaching, learning, administration, and research. This coordinated, collective, and strategic approach has put Harvard ahead of many peers in higher education.

Anand: Teaching and learning are fundamentally decentralized activities. So our approach was to ask: First, how can we ensure that local experimentation by faculty and staff is enabled as much as possible; and second, how can we ensure that it’s consistent with University policies on IP, copyright, and security? We also wanted to ensure that novel emerging practices were shared across Schools, rather than remaining siloed.

What do these tools mean for faculty, in terms of the challenges they pose or the opportunities they offer? Is there anything you’re particularly excited about?

Anand: Let’s start with some salient challenges. How do we first sift through the hype that’s accompanied GenAI? How can we make it easy for faculty to use GenAI tools in their classrooms without overburdening them with yet another technology? How can one address real concerns about GenAI’s impact?

While we’re still early in this journey, many compelling opportunities — and more importantly, some systematic ways of thinking about them — are emerging. Various Harvard faculty have leaned into experimenting with LLMs in their classrooms. Our team has now interviewed over 30 colleagues across Harvard and curated short videos that capture their learnings. I encourage everyone to view these materials on the new GenAI site; they are remarkable in their depth and breadth of insight.

Here’s a sample: While LLMs are commonly used for Q&A, our faculty have creatively used them for a broader variety of tasks, such as simulating tutors that guide learning by asking questions, simulating instructional designers to provide active learning tips, and simulating student voices to predict how a class discussion might flow, thus aiding in lesson preparation. Others demonstrate how more sophisticated prompts or “prompt engineering” are often necessary to yield more sophisticated LLM responses, and how LLMs can extend well beyond text-based responses to visuals, simulations, coding, and games. And several faculty show how LLMs can help overcome subtle yet important learning frictions like skill gaps in coding, language literacy, or math.

Do these tools offer students an opportunity to support or expand upon their learning?

Anand: Yes. GenAI represents a unique area of innovation where students and faculty are working together. Many colleagues are incorporating student feedback into the GenAI portions of their curriculum or making their own GenAI tools available to students. Since GenAI is new, the pedagogical path is not yet well defined; students have an opportunity to make their voices heard, as co-creators, on what they think the future of their learning should look like.

Beyond this, we’re starting to see other learning benefits. Importantly, GenAI can reach beyond a lecture hall. Thoughtful prompt engineering can turn even publicly available GenAI tools into tutorbots that generate interactive practice problems, act as expert conversational aids for material review, or increase TA teams’ capacity. That means both that the classroom is expanding and that more of it is in students’ hands. There’s also evidence that these bots field more questions than teaching teams can normally address and can be more comfortable and accessible for some students.

Of course, we need to identify and counter harmful patterns. There is a risk, in this early and enthusiastic period, of sparking over-reliance on GenAI. Students must critically evaluate how and where they use it, given its possibility of inaccurate or inappropriate responses, and should heed the areas where their style of cognition outperforms AI. One other thing to watch out for is user divide: Some students will graduate with vastly better prompt engineering skills than others, an inequality that will only magnify in the workforce.

What are the main questions your group has been tackling?

Anand: Our group divided its work into three subgroups focused on policy, tools, and resources. We’ve helped guide initial policies to ensure safe and responsible use; begun curating resources for faculty in a One Harvard repository ; and are exploring which tools the University should invest in or develop to ensure that educators and researchers can continue to advance their work.

In the fall, we focused on supporting and guiding HUIT’s development of the AI Sandbox. The Harvard Initiative for Learning and Teaching’s annual conference , which focused exclusively on GenAI, had its highest participation in 10 years. Recently, we’ve been working with the research group to inform the development of tools that promise broad, generalizable use for faculty (e.g., tutorbots).

What has your group focused on in discussions so far about generative AI tools’ use in research?

Shaw: Our group has some incredible strength in researchers who are at the cutting edge of GenAI development and applications, but also includes voices that help us understand the real barriers to faculty and students starting to use these tools in their own research and scholarship. Working with the other teams, we have focused on supporting development and use of the GenAI sandbox, examining IP and security issues, and learning from different groups across campus how they are using these tools to innovate.

Are there key areas of focus for your group in the coming months?

Shaw: We are focused on establishing programs — such as the new GenAI Milton Fund track — to help support innovation in the application of these tools across the wide range of scholarship on our campus. We are also working with the College to develop new programs to help support students who wish to engage with faculty on GenAI-enabled projects. We aim to find ways to convene students and scholars to share their experiences and build a stronger community of practitioners across campus.

What types of administration and operations questions are your group is exploring, and what type of opportunities do you see in this space?

Jelinkova: By using the group to share learnings from across Schools and units, we can better provide technologies to meet the community’s needs while ensuring the most responsible and sustainable use of the University’s financial resources. The connections within this group also inform the guidelines that we provide; by learning how generative AI is being used in different contexts, we can develop best practices and stay alert to emerging risks. There are new tools becoming available almost every day, and many exciting experiments and pilots happening across Harvard, so it’s important to regularly review and update the guidance we provide to our community.

Can you talk a bit about what has come out of these discussions, or other exciting things to come?

Jelinkova: Because this technology is rapidly evolving, we are continually tracking the release of new tools and working with our vendors as well as open-source efforts to ensure we are best supporting the University’s needs. We’re developing more guidance and hosting information sessions on helping people to understand the AI landscape and how to choose the right tool for their task. Beyond tools, we’re also working to build connections across Harvard to support collaboration, including a recently launched AI community of practice . We are capturing valuable findings from emerging technology pilot programs in HUIT , the EVP area , and across Schools. And we are now thinking about how those findings can inform guiding principles and best practices to better support staff.

While the GenAI groups are investigating these questions, Harvard faculty and scholars are also on the forefront of research in this space. Can you talk a bit about some of the interesting research happening across the University in AI more broadly ?

Shaw: Harvard has made deep investments in the development and application of AI across our campus, in our Schools, initiatives, and institutes — such as the Kempner Institute and Harvard Data Science Initiative. In addition, there is a critical role for us to play in examining and guiding the ethics of AI applications — and our strengths in the Safra and Berkman Klein centers, as examples, can be leading voices in this area.

What would be your advice for members of our community who are interested in learning more about generative AI tools?

Anand: I’d encourage our community to view the resources available on the new Generative AI @ Harvard website , to better understand how GenAI tools might benefit you.

There’s also no substitute for experimentation with these tools to learn what works, what does not, and how to tailor them for maximal benefit for your particular needs. And of course, please know and respect University policies around copyright and security.

We’re in the early stages of this journey at Harvard, but it’s exciting.

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Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

At a Glance

Start Date May 2024

Application Deadlines Final*: April 9, 2024

*A limited number of partial scholarships are still available. Apply by the final deadline to receive initial consideration for these awards.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

How To Solve MATLAB Problems Efficiently

Explore the functionalities of 'solve' in MATLAB, a pivotal tool for developers. This article elucidates how to leverage this feature to solve algebraic equations efficiently, offering practical examples and addressing common issues encountered, all aimed at refining your coding skills in MATLAB.

MATLAB is a widely-used platform for numerical computing and visualization. While it offers a vast array of functionalities, many programmers face challenges when trying to solve specific problems. This article aims to provide insights and techniques to help you navigate through these challenges more effectively.

💡KEY INSIGHTS

• The efficient use of MATLAB's 'solve' function greatly enhances problem-solving capabilities for algebraic equations, demonstrating its versatility in various computational scenarios.
• Understanding and avoiding common errors in MATLAB, such as matrix dimension mismatches and undefined variables, is crucial for smoother coding experiences.
• Optimization techniques such as preallocating arrays and vectorization are essential for improving the performance of MATLAB code, especially with large datasets.
• Adopting best programming practices , including descriptive variable naming and effective error handling, significantly enhances code readability and maintainability in MATLAB.

Understanding MATLAB Basics

Setting up your matlab environment, common errors and their solutions, optimizing matlab code for efficiency, best practices for matlab programming, frequently asked questions, variables and data types, basic operations, functions and scripts, plotting and visualization.

MATLAB, which stands for Matrix Laboratory , is a high-level programming language and interactive environment primarily used for numerical computations, data analysis, and visualization. At its core, MATLAB operates using matrices, making it particularly powerful for linear algebra operations.

In MATLAB, you can create a variable simply by assigning it a value. For instance, a = 5; assigns the value 5 to the variable a . MATLAB supports various data types, including scalars, vectors, matrices, and more complex structures.

MATLAB supports a wide range of mathematical operations. From basic arithmetic to more complex matrix manipulations, the language is designed to handle numerical data efficiently.

MATLAB provides numerous built-in functions, but you can also define your own. Functions are blocks of code designed to perform specific tasks and can return values.

In addition to functions, MATLAB supports scripts, which are sequences of commands saved in a file. Scripts don't accept input arguments or return output, but they're useful for automating repetitive tasks.

One of MATLAB's strengths is its plotting capabilities. With just a few lines of code, you can visualize data in various formats.

Installation And Licensing

Workspace and directory, toolboxes and add-ons, setting preferences, creating a startup script.

Before diving into MATLAB programming, it's essential to set up your environment correctly. This ensures that you have all the necessary tools and libraries at your disposal, making your coding experience smooth and efficient.

To start with MATLAB, you first need to install the software. MATLAB is a licensed product, so ensure you have a valid license key before beginning the installation.

• Visit the official MATLAB website and download the latest version.
• Run the installer and follow the on-screen instructions.
• When prompted, enter your license key to activate the product.

Once installed, familiarize yourself with the MATLAB workspace . This is where all your variables, data, and scripts will be stored during a session. The current directory pane shows the files in your current folder, making it easy to access and manage your scripts and functions.

MATLAB offers various toolboxes tailored for specific applications, from signal processing to machine learning. To enhance functionality, you can install these toolboxes based on your project's requirements.

Customizing your MATLAB environment can boost your productivity. From changing the editor's appearance to modifying default settings, MATLAB allows for extensive customization.

If you have specific commands or initializations you want to run every time MATLAB starts, you can create a startup script .

Matrix Dimension Mismatch

Undefined function or variable, mismatched parentheses or brackets, using reserved keywords, file not found, incorrect function arguments.

While MATLAB is a powerful tool, like any programming environment, you're bound to encounter errors. Understanding these errors and knowing how to troubleshoot them is crucial for efficient coding.

One of the most common errors in MATLAB is a matrix dimension mismatch, especially when performing matrix operations.

If you try to use a variable or function that hasn't been defined, MATLAB will throw an error.

Forgetting to close a parenthesis or bracket is a common oversight.

MATLAB has several reserved keywords. Using these as variable names can lead to unexpected behaviors.

When trying to load a file or run a script, you might encounter a "file not found" error.

Passing the wrong number or type of arguments to a function can lead to errors.

Preallocating Arrays

Vectorization, avoiding global variables, using built-in functions, sparse matrices, profiling your code.

MATLAB is inherently fast due to its matrix-based nature. However, there are times when specific operations or large datasets can slow down your code. By following some best practices, you can ensure your MATLAB code runs as efficiently as possible.

One of the simplest ways to speed up your MATLAB code is by preallocating memory for arrays, especially when they're large.

MATLAB is optimized for matrix operations. Whenever possible, use vectorized operations instead of loops.

Global variables can slow down MATLAB functions because they require additional memory access. Instead, pass variables as function arguments.

MATLAB's built-in functions are highly optimized. Whenever possible, use them instead of writing custom code.

If you're working with matrices that have a lot of zeros, consider using sparse matrices . They store only non-zero elements, saving memory and computational power.

MATLAB's built-in profiler can help identify bottlenecks in your code. By analyzing your code's performance, you can pinpoint areas that need optimization.

Descriptive Variable Names

Consistent indentation, commenting and documentation, avoid hardcoding values, modularize your code, error handling.

While MATLAB is a versatile tool for numerical computing, following certain best practices can make your code more readable, maintainable, and efficient. These practices not only enhance your coding experience but also ensure that others can understand and work with your code seamlessly.

Using descriptive variable names makes your code more readable and self-explanatory. Avoid using generic names like temp or data .

Consistency in indentation helps in visually separating different blocks of code, making it easier to follow the logic.

While it's essential to keep your code clean, adding relevant comments can be invaluable. Comments should explain the why, not the how.

Instead of hardcoding specific numbers or values, use variables or constants. This makes it easier to update or modify your code later.

Break your code into smaller, reusable functions. This not only makes your code more readable but also reduces redundancy.

Incorporate error handling in your code to manage unexpected inputs or situations gracefully.

How is MATLAB different from other programming languages like Python or R?

MATLAB is specifically designed for numerical and matrix-based computations. While Python and R have libraries that offer similar functionalities, MATLAB provides a dedicated environment with built-in functions optimized for mathematical operations.

Do I need to know programming to use MATLAB?

While prior programming knowledge can be beneficial, MATLAB is designed to be user-friendly for beginners. Its syntax is straightforward, and there are numerous resources and tutorials available to help newcomers get started.

What is MATLAB used for?

MATLAB is a high-level programming language and environment primarily used for numerical computing, data analysis, and visualization. It's widely used in engineering, physics, finance, and other fields for tasks like signal processing, machine learning, and statistical modeling.

Can I run MATLAB on any operating system?

Yes, MATLAB is available for Windows, macOS, and Linux. Ensure you download the appropriate version for your operating system from the official website.

Is MATLAB free?

No, MATLAB is a licensed product. However, MathWorks offers a free trial, and there are discounted student versions available. Some institutions also provide MATLAB access to their students and faculty.

Let’s test your knowledge!

What Does 'solve' Function Do In MATLAB?

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Bridging the Gap between Theory and Practice: Solving Intractable Problems in a Multi-Agent Machine Learning World - Elmore Family School of Electrical and Computer Engineering - Purdue University

Bridging the Gap between Theory and Practice: Solving Intractable Problems in a Multi-Agent Machine Learning World

Emmanouil-Vasileios (Manolis) Vlatakis Gkaragkounis is currently a Foundations of Data Science Institute (FODSI) Postdoctoral Fellow at the Simons Institute for the Theory of Computing, UC Berkeley, mentored by Prof. Michael Jordan. He completed his Ph.D. in Computer Science at Columbia University, under Professors Mihalis Yannakakis and Rocco Servedio, and holds B.Sc. and M.Sc. degrees in Electrical and Computer Engineering. Manolis specializes in the theoretical aspects of Data Science, Machine Learning, and Game Theory. His expertise includes beyond worst-case analysis, optimization, and data-driven decision-making in complex environments. Applications of his work span multiple areas from privacy, neural networks, to economics and contract theory, statistical inference, and quantum machine learning. 

David Inouye, [email protected] 

2024-04-10 08:00:00 2024-04-10 17:00:00 America/Indiana/Indianapolis Bridging the Gap between Theory and Practice: Solving Intractable Problems in a Multi-Agent Machine Learning World Manolis Vlatakis UC Berkeley 1:30 pm

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Computer Science > Computation and Language

Title: saas: solving ability amplification strategy for enhanced mathematical reasoning in large language models.

Abstract: This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplification of problem-solving ability. Thus, the initial learning with CoT is essential for solving challenging mathematical problems. To this end, we propose a sequential learning approach, named SAAS (Solving Ability Amplification Strategy), which strategically transitions from CoT learning to PoT learning. Our empirical study, involving an extensive performance comparison using several benchmarks, demonstrates that our SAAS achieves state-of-the-art (SOTA) performance. The results underscore the effectiveness of our sequential learning approach, marking a significant advancement in the field of mathematical reasoning in LLMs.

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