Python programming roadmap - what skills should you learn first

Key Takeaways

Most people learning Python are doing it completely backwards. They watch tutorial after tutorial, build nothing, and then they wonder why they still feel like a beginner months later. Now, if this sounds like you, this video will fix that. I'm going to give you a clear road map on exactly what skills to learn, in what order, so you can actually become a capable Python developer. Let's dive into it. So, let's begin with why you should learn Python. Now, Python is one of the best languages to start with, and here's why. It's versatile, so you can use it for webdev, AI, automation, scripting, games, data science, pretty much anything. It's in massive demand globally. It consistently ranks in the top three programming languages for job postings, and the average Python salary is over $100,000 in the United States. It's also readable, beginnerfriendly, and great for personal or professional projects. So you're not wasting your time by learning Python, but you are wasting your time if you don't follow a plan. Additionally, you need to make sure that while you're learning this, you follow the 8020 rule where you spend 80% of your time actually doing writing code and 20% of your time learning or following tutorials. So with that said, let's get into phase one, which discusses the first things you need to learn related to Python, which is the core Python fundamentals. Okay, so the first step here is learning the core syntax of Python. Now, this should take you one to two weeks, not months to do. So here's what you need to focus on. Variables, data types, lists, dictionaries, and tpples. Okay? Then you need to look at operators. So conditional operators and arithmetic operators and things like type conversions. This is how you do math, for example, how you work with different data types, how you can convert them, and how you can compare different objects. You then need to look at conditional statements. So things like if statements, else statements, l if statements and then loops, specifically for loops and while loops, which are fundamental to every programming language. Then some more advanced features like slices and exceptions, things like mutability. So if objects can change or if they stay the same, and then functions, of course. Now once you learn that, you also need to look at object-oriented programming, specifically classes, objects, and methods. and then how you create various classes and what that means, what object-oriented programming means in the context of Python. Now, one other thing I want to say here is don't jump into frameworks or things like data science just yet. Just learn the language. Pick one resource, whether that's a YouTube channel, Udemy course, whatever. It doesn't matter. Or even follow the official documentation, but just follow it from start to finish. Do the exercises, predict the output, break the code, and fix it. And if you're going to watch videos, then make sure you constantly challenge yourself and try to guess what's going to happen next. Okay, this is the way that you watch videos. You have a video open on one side of your screen. You have your code editor on the other. And you're constantly predicting what's going to happen, playing with the code in the video, actually writing things out. You can't learn by watching. You need to learn by doing. Speaking of which, that brings me to phase two, which is to work on small projects and to practice oop object-oriented programming. So, at this point, you need to start applying what you've learned. This is where you're actually going to get comfortable writing real code, especially in an object-oriented programming style. Now, you should learn the following. How to split code into multiple files, so things like packages and modules. How to write reusable classes and functions, so you can use them multiple times. How to handle errors with things like try, accept, and finally clauses. And then, how to save and load data from files, so things like basic data persistence. And then of course best practices of programming. So how do you make a simple plan? How do you come up with the code you actually need to write for a project? That's what you need to focus on here. Take the theory of the language, apply it into some small projects. And I'm going to give you a few ideas here that you can work on which will help you do this. So first I have a quiz application. Okay? So you're going to ask the user a bunch of different questions. They're going to answer it with text. You're going to store what their answers are and calculate how many questions they got right. Next I have an inventory tracker. So you can store the different quantities of objects. You can track them by IDs. You can delete objects, remove objects, add new objects. You get the idea. Just a basic inventory system that works in your terminal. And then I have something like a text adventure game. So this will allow you to have some player. They're navigating through maybe different rooms. You give them some choices that they have to pick. And the idea here is that these don't have to be perfect projects, but you're trying to struggle and work on small things that are digestible that help you get better at the language. Now, here's the thing. Studies have shown that when you're just watching tutorials or reading blog posts, you only absorb about 20% of the material. When you learn actively by actually writing code and building things, that retention jumps to as high as 75 to 90%. And that's a massive difference. And that's exactly why I recommend learning Python interactively right from day one. Now, one of the best ways to do that is with Data Camp, who's sponsoring today's video. Their platform is built around hands-on coding where you write real Python code directly in your browser and you get instant feedback as you go. Now, Data Camp has two tracks that are perfect if you're getting started. The first is Python programming fundamentals where it walks you through the essentials like variables, functions, and data handling. And then there's the associate Python developer track. This one steps things up with real world topics like APIs, data structures, and debugging. It's everything you need to become job ready with Python. Now, I've been using and recommending Data Camp for a while now, and it's honestly something that I wish I had when I was just getting started learning. Both tracks are practical, interactive, and designed to actually help you learn by doing, not just by watching and forgetting. And right now, you can get 25% off using my link below. So, if you're serious about getting good at Python, now is the time to jump in. So, now we move to phase three, which is to learn real developer tools. Okay, so it's time to learn the tools that developers use like Git and GitHub for version control. Virtual environments like Venv, or UV. I recommend going with UV. Debugging tools. So things like using the print statement for debugging. Understanding the logging and things like using the debugger within your IDE specifically for Python code. Then you need to learn about things like pip. How do you install various different packages? How does that fit in with your virtual environment? Things like the requirements.ts txt file, things like Python modules and imports and more about the Python ecosystem. Then I recommend getting comfortable with the terminal. So basic commands like changing directories, printing out the working directory, modifying different files, deleting files, copying, etc. Now, you don't need to be an expert, but basic Linux commands can help you a lot. Now, one thing you definitely want to do in this section here is start version controlling every project that you write. So, you want to make meaningful commits. You want to push your code up to GitHub. And you want to understand what it's actually like to work with version control and to checkpoint your projects and save what you're doing. This might seem unnecessary when you're working alone, but I promise it's a really good habit to build. And you're going to use Git your entire life from this point forward. So, you might as well get good at it. Okay. Now, that leads us to phase four. You've learned the basics of the language. You've learned the developer tools. You've built a few mini projects. Now it's time to actually build something real. What you need to do here is to pick one area or kind of niche within Python development and stick to it for at least 30 days. Now this means using Python to build something real which requires learning some Python modules. So there's a few options for you. You could go into web development where you look at something like Flask, fast API or Django. You could go into data analysis where you look at things like pandas, numpy and mapplot lip. You could go into automation where you do things like write basic scripts, work with selenium, playright, puppeteer, web browser automation, etc. You could learn about APIs, so things like requests, JSON, external services, how you interact with APIs and calls, call them, sorry. You can get into AI or LLMs where you learn about Langchain, Langflow, Olama, Transformers, Hugging Face, etc. There's a bunch of different areas you can pick here, but you need to pick one. Not all of them, just one. So you can actually learn something meaningful with these modules and build a real project, which is what Python is actually used for. Okay, I'm going to give you a few examples of projects you could build. You could build a simple blog using Flask or Django where you have users who can sign in and sign out and make posts. You could build a YouTube data scraper and dashboard. You could build a price tracking bot that emails you alerts. You could build a simple AI agent with Langchain that allows you to summarize and kind of study articles. doesn't matter. Okay? But you pick one of these areas, go deep into it, learn a lot about it, and learn those specific Python modules so you can actually become productive and build a real project. Okay? Now, that leads me to phase five, which is to become Pythonic. Now, this is time to level up and start learning the unique features of Python. Up until this point, most of what you've learned isn't unique to Python. It's just basic programming principles, which you should start with. But Python is a little bit unique and has some features that you definitely should know if you want to get good at it. So first we have list comprehensions. Then we have generator expressions, things like context managers, so with statements, so with this, using this, etc. You have decorators. These are little at symbol things that go above your functions. You have star args and star quarks. Things like type hints, dock strings, etc. There's a lot of advanced Python features, but those are the core ones that you definitely need to learn and understand how they work within the Python ecosystem. Now, what you can do here to learn this is you can take an old project of yours and you can refactor it to use some of these new techniques that you've learned. This will allow you to learn how to write cleaner, faster, and more maintainable code and give you a solid review of kind of why your code wasn't good before and how you could fix it now with your new knowledge. Now the advanced Python developers obviously use these features all the time. So you need to learn them. Okay. Now this leads me to phase six which is some more advanced concepts. Now at this point you already know a lot about Python but obviously if you want to become better there's some more advanced features that are a bit trickier to learn. So what I recommend here is the following. First we want to dive into threading versus multiprocessing. Okay. So we want to learn when to use threads for input outputbound tasks and multipprocessing for CPUbound tasks. Now this is especially important in the context of Python because Python has something called the global interpreter lock. It's not as relevant in the newer versions of Python, but that's definitely something you need to learn about to understand the performance impact of running Python code and how to make that more scalable. Then async IO. This is very popular in newer versions of Python. You need to understand this so that you know how to do asynchronous operations specifically when you're building web applications calling various APIs etc. And then like I said global interpreter lock you need to understand what this is and why it limits multi-threaded applications for Python and how to get around that. Lastly I recommend looking at various different Python versions. You should know the core differences between things like Python 3.8, 8, Python 2, Python 3.13 because there's a lot of new features that were added that won't work in more legacy versions of Python. Then I also suggest looking into various Python runtimes or interpreters/compilers. So things like CPython versus Pi versus MicroPython and learning how these different implementations can affect the performance of Python. Now understanding this gives you a massive edge in writing performant, scalable and maintainable Python code. And it's really the next level to understand the inner workings of Python. Okay. Now, my final thoughts here. If you've been learning Python for months and you haven't finished anything real, then you're doing it wrong. Learn a little bit, build something, get stuck, learn what you need, finish, and repeat that process. That's how real developers level up. It's not by consuming content endlessly. It's by solving problems, building cool stuff, breaking things, and then fixing it and just repeating and repeating and repeating. That's how I got good at Python. If you guys enjoyed this video, make sure to leave a like, subscribe to the channel, and I will see you in the next one. [Music]