If you were to start learning to code today, where would you begin? Some developers who started over 6 years ago took at least 3 years to feel confident in their skills. But today, things are very different. You can reach an intermediate level in Python in just 3 months or less if you learn it the right way. In this article, I'm going to show you exactly how to learn Python if you were starting from scratch today.

Is Python Still Relevant in the Age of AI?

Before we dive in, let's address the elephant in the room: is it still worth learning Python in the age of artificial intelligence? What's the point when AI can write code much faster than we can? Well, Python is still one of the most in-demand programming languages today. In addition, knowing how to program in Python empowers you to build applications on top of AI models. This means you're not just a consumer of AI; you can build things with it.

Yes, AI can now generate code, making coding more accessible with no-code and low-code tools. But it still has significant limitations that we can't ignore. Hallucinations and inaccuracies may be amusing in some contexts, but in coding, they can be fatal. A cybersecurity researcher recently noticed that large language models repeatedly produced a command to install a non-existent Python package. Without sufficient understanding, you might unknowingly allow AI-generated code to install malware in your environment. That's why you still need to know how to code, even if you use AI to write it. Being proficient in Python will help you verify and correct AI-generated code and leverage AI effectively and safely.

Programming is about logical thinking and using that creativity to create a set of instructions. It helps that Python's syntax is clean and resembles plain English, making it relatively easy to read even for those who don't know how to code.

Choosing Your Development Environment

Choosing the right development environment can make your life a little bit easier. The best choice depends on your goals. Do you want to learn Python to become a developer, or do you want to learn it for data analysis, machine learning, or AI? You can also choose between a local code editor and a hosted service.

At the very beginning, you may not want to bother with installing Python and setting up an environment yourself. You can use an online code editor such as Replit, where you can start writing code right away. For data science and machine learning, a common tool is the Jupyter Notebook, which allows you to run blocks of code individually and inspect the results. An online version of Jupyter Notebook is Google Colab, a hosted service that requires no setup and provides free access to computing resources. This is a great option for small projects and real-time collaboration.

However, in the long run, it's best to install Python on your computer so you can use it locally. You can then use Python directly in your terminal. Try using it as a calculator or printing a fun joke. The next step is to install an Integrated Development Environment (IDE) like Visual Studio Code or PyCharm. These are software applications that provide comprehensive facilities for software development, making it easy to edit code and offering all the functionalities you might need for your project.

While Jupyter Notebooks are great for data science projects, using them inside Visual Studio Code can be even more powerful, especially with extensions like GitHub Copilot, which we'll discuss later in this article.

Mastering the Fundamentals

Once you've chosen your tools, you can immediately start learning the basics: variables, data types, and functions. It's crucial to understand control flow, such as conditional statements and loops. Don't get bogged down in the details of minor topics, as this can lead to a loss of motivation.

To help you visualize your learning path, here’s a breakdown of Python concepts, divided into basic, intermediate, and advanced topics.

1. Basic Topics: These include setting up your IDE, installing and managing packages, understanding the working directory, and core programming concepts like: - Data Types - Variables - Functions - Operators - Conditional Statements - Loop Statements It's fine not to completely understand everything at this stage; you'll have plenty of opportunities to practice and solidify your knowledge.

2. Intermediate Topics: This is where you can start doing really useful things. On this level, you should learn more advanced concepts like: - Working with different file types - Version Control with Git - Python for Data Science (Data Analysis and Machine Learning) - Object-Oriented Programming (OOP) - Decorators - Debugging and Error Handling

At this stage, you also want to focus on good coding practices, which means writing clean, readable, and efficient code. There's a significant difference between writing code for a personal project and building a real-world application. Some of the most important coding practices include: - Follow a Style Guide: Adhere to the PEP 8 style guide for Python code and be consistent. - Use Meaningful Variable Names: Naming variables is one of the hardest things in programming, but it's crucial for readability. - Avoid Hard-Coding: Don't hard-code numbers in your code, as no one else (including your future self) will understand what they represent. - Use List Comprehensions and Generators: When appropriate, use these instead of traditional for loops for more concise and efficient code. - Implement Error Handling: Add robust error handling to your code. - Comment and Document: Provide comments and documentation to explain your code. - Use Virtual Environments: Encapsulate packages for separate projects using virtual environments. - Write Unit Tests: Create unit tests for your functions to ensure they work as expected.

There's a lot to learn, so it's a good idea to start thinking about your specialization. - For Data Science, Machine Learning, and AI: Master the basics and then focus on essential data science packages like NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn. - For Data Engineering or Software Engineering: Object-Oriented Programming, decorators, and writing efficient, clean code are even more critical.

3. Advanced Topics: Once you've mastered the intermediate topics, you can start building more complex applications. This includes: - Using APIs - Developing web applications with user interfaces (using frameworks like Django and Flask) - Building complex games - Deploying applications on cloud hosting platforms This is the stage where you move from building prototypes in Jupyter Notebooks to creating user-friendly applications.

You can use this roadmap as a reference for building your own learning curriculum based on your goals and needs. After that, you can start looking up tutorials or signing up for an online Python course to go through the concepts you want to learn.

The Learn-Do-Teach Method

This overview is just a guide. The best way to actually learn something is through doing, and the best way to truly own your knowledge is through teaching. Let me explain. Most of us learn by consuming information, moving from one topic to the next. By the time you get to the fourth or fifth topic, you've already forgotten the first few. This isn't because you're not smart; it's because if you learn something without immediately applying it, your brain gets a signal that the information isn't important.

A better way to learn Python (or any new skill) is to immediately put what you've learned into practice and create something useful with it. It doesn't have to be a world-changing project. For example, if you just learned about Python functions, you could create a function to calculate your BMI. ```python def calculatebmi(weightkg, heightm): """Calculates Body Mass Index (BMI).""" bmi = weightkg / (height_m ** 2) return bmi

Example usage:

mybmi = calculatebmi(70, 1.75) print(f"Your BMI is: {my_bmi:.2f}") ``` If you need ideas, you can simply ask an AI assistant to create a quiz or practice problem for you.

At this point, you might think you can move on to the next topic. Or, you could take it to the next level: teach others what you've learned. Writing blog posts and tutorials to explain concepts you've just learned is a powerful technique. By teaching, you learn more deeply and reveal gaps in your knowledge that you would otherwise never know. It's been said that no one learns as much about a subject as one who is forced to teach it. So, the best formula for learning to code is: Learn, Do, Teach.

Overcoming the Valley of Despair

This is easier said than done. Almost all of us experience a dip in confidence and motivation while learning a new skill, and coding is no exception. You get started, feeling excited after printing "Hello, World!" and writing your first for loop. You feel like you're crushing it and on your way to becoming a guru. But this is often an illusion known as the Dunning-Kruger effect, where people with low ability at a task overestimate their ability.

After a week or two, you start feeling overwhelmed and realize how little you actually know. You want to learn Python for machine learning, but then you realize you also need to learn math, statistics, and computer science concepts. This is completely normal. Unfortunately, many people give up at this stage. If you can't push through this phase, you'll forever be a beginner. In today's world, being a beginner means you're less competent than an AI. All you need to do is to trust the process.

The best way to get out of this 'valley of despair' is to learn with a purpose. Find a problem you want to solve and create a personal project that addresses it. This will shift your focus from 'I'm incompetent' to 'I'm learning what it takes to solve a real-life problem.'

You might be asking, 'But I'm still just a beginner. Where do I start?' Your project doesn't need to be complicated or change the world; it just needs to be a little bit useful. For example, one interesting project could be to analyze the text from a book series to create a network graph of character interactions. It's fun, doable, and will expose you to numerous new ideas and concepts. That's the point.

When you get absorbed in your project, you'll be too excited to stop. You might find yourself coding into the early hours of the morning just to see if something works and watch your project slowly come to life. There's no better source of motivation than this.

You'll also come across many similar projects from other people. You can reverse-engineer what they've done to tackle the same challenges, which will help you build your own problem-solving skills. In the age of AI, you no longer have to do projects all by yourself. Tools like GitHub Copilot can help you write code faster and with fewer errors. In data science, this allows you to focus more on the ideas and creative process rather than getting bogged down with fixing data types or adjusting chart axes, which can be very time-consuming.