How I would learn Python programming FAST (If I could start over)

Key Takeaways

I've been writing Python for over 10 years. I've taught millions of people how to code through this channel, and if I could go back and start over, I would do almost everything completely different. Now, it's not because the language has changed, but because I wasted so much time learning the wrong things in the wrong order. I went super broad when I should have gone deep, and it cost me months of time. And right now, Python developers are some of the most hireable people in tech. It tops every language ranking and is a requirement in a majority of all developer positions, especially if we're talking about AI. So, here's the exact path that I would follow today if I was starting Python from zero and wanted to get good fast. Let's dive in. Now, I want to start by talking about the biggest mistakes that I made when I was learning Python. Now, the first is that I tried to learn everything at once. So, web development, game development, data science, scripting, automation, you name it. I was a mile wide and just an inch deep. Now, what I did back then is I'd start with a Flask project, right? Get halfway through it, then jump over to Pygame, then start messing with data analysis, then circle back to web scraping. Now, none of this stuck because I never actually finished anything. And the result of this was after months of learning Python, I couldn't confidently build a single complete project in any one of these areas. Now, what I should have done instead was master the fundamentals first, then pick one direction and go deep. Now, specialization beats generalization, especially early on, and that's what I'm going to talk about in this video. Anyways, here is the order that I would learn things in, what I'd skip, and the mindset shift that would have saved me the most time when I was starting with Python. So, let's start with step one, which is the core fundamentals. Now, this is a non-negotiable. Before you touch any framework or library in Python, you need to be fluent in the basics. So, you want to spend your starting time, probably the first month or so, learning things like variables, data types, strings, numbers, booleans, right? Control flow. So, if else, for loops, while loops, then moving to functions. So, how you define them, what a parameter is, what an argument is, what a return value is. Continuing, you'll want to look at data structures. So, lists, dictionaries, tuples, sets, right? And know which is which and when to use each. You also want to look at dictionaries, for example, which show up a lot in Python code. Next, you're looking at list comprehensions. Super Python specific, but also incredibly useful. You want to learn them early on so you can understand them when you see them in code later on. You also want to look at file IO, so reading and writing files. Again, pretty simple, but you're going to use it all the time. You want to look at error handling. So, try, except, finally blocks. What do those mean? And the goal at this stage is that you want to build or write a small program. We're talking maybe 100, 200 lines of code from scratch without having to look at the syntax every 30 seconds. Now, we're not assuming that you're going to memorize everything, but the goal here is to look at the core features, get the fundamentals down, and be comfortable reversing a list, be comfortable writing a small program that asks a user to guess their favorite number, be comfortable writing a choose your own adventure game, right? Basic projects, but something that you can do in two to four weeks of time, especially if you're coding every day, you're following a curriculum, and you're not rushing through it, and you're trying to understand everything. So, once you master the fundamentals, we move on to step two or section two, which is object-oriented programming. Now, honestly, this is where a lot of self-taught developers have a pretty big gap. Now, you can actually get surprisingly far without learning object-oriented programming, but especially if you want to get good at Python, you're going to hit the ceiling fast. So, what I'm talking about here is classes, objects, initialization, method, attributes, inheritance, right? What it is, when to use it, when not to use it. Dunder methods, so things like dunder string, dunder wrapper, dunder len. This is two underscore something and then two underscores, and this is a really fundamental part of how Python operates. Now, you also want want when to use OOP versus just when to use normal functions, and when something should be a class, first when it could just be a normal variable, for example. And the key here is that OOP can keep your code more organized, and by learning it properly, you can actually understand how Python works on a lower level, and especially how operations like addition, indexing, getting the length of something actually work. This is super fundamental, and once you master OOP, you'll find you have a much deeper understanding of the Python language as a whole. Now, once you've had a look at OOP, you want to move on to step three, which is modules, packages, and virtual environments. Now, this is how you organize code across multiple files. So, things like import statements, what's, you know, underscore underscore name underscore underscore equals underscore underscore main underscore underscore means, things like pip and installing various packages, virtual environments, what UV is, for example, how you actually install things and manage the dependencies of your projects. You then want to look at how you can understand and read documentation for a simple package that you've never used before. Bringing in some built-in modules in Python, like random or OS or JSON, right, and getting comfortable using code that you maybe never seen in a tutorial before. Now, these are things that often aren't going to be taught, and that you need to really go out there and look for, and say, "Hey, how can I turn this, you know, 500-line Python script into a few separate files? How can I organize this?" And at this point, what's going to happen is you're going to start having all of the raw Python skills, and it's really about how you apply them, how you organize them, and how you start working on some larger projects that have various dependencies, environment variables, all of that kind of stuff. So, that's step three, where you're really looking at the organization and structure of your code, and kind of the dependency and environment management. Now, if you're still at the beginning of this journey, and you want a structured way to build all of these fundamentals without bouncing between random tutorials, then check out DataCamp. Now, I've partnered with them for this video, and I've personally been using them to develop my own Python skills for years. Now, the track that I'd point you to is their Python Programming Fundamentals track. It covers everything I just talked about, the core syntax, data structures, functions, and it builds up in a logical order so you're not just jumping around. Now, the thing that sets DataCamp apart for me is that you're actually writing code the entire time. Every lesson has you coding in the browser, solving problems, and not just watching someone else type. Now, that's how Python programming actually sticks. And then once you've built that foundation, you can keep leveling up with DataCamp's Python certification program. Now, this can turn your Python skills into something you can actually show on your resume or LinkedIn. Now, when I was starting out, my problem was exactly what I described, no structure, no clear path, and just random projects and tutorials. Having a track like this takes you from zero through the fundamentals in the correct order, and it would have saved me a massive amount of wasted effort. DataCamp is used by over 19 million learners, and you can start for free with the link in the description. You can also get 25% off any track using my link below. Anyways, let's talk about what comes after the fundamentals. Now, this is step four, and this is where you need to pick a direction and go deep. Now, this is probably the most important part of this video, and the one thing that I really got wrong when I was starting. Once you've mastered the fundamentals and you've gone through steps one to three, you need to pick one area and commit to it for at least three to six months. Now, the main options that you have here are web development, so we're talking about like Django, Flask, FastAPI, data science, for example, we have machine learning, NumPy, Pandas, scikit-learn, PyTorch. We have automation and scripting, so it's like file handling, APIs, web scraping. We have game development, even though it's not really used for game development in Python, but you could totally do that to get better, things like Pygame, for example. And we even have back-end development, right? Where you're doing API development using things like FastAPI. There's a lot of different options here, but the key is that you want to pick one particular area and go deep, because you're not going to be employable or really just even have a good skill, unless you focus in one area. If you do what I did and you jump between all of them, you're never going to get good at one. By going deep, it means that you're going to be able to build full projects from scratch in that area. You're going to understand the common patterns, the pitfalls, and you can actually go and do an interview about this because you've been using it for a fair amount of time. Now, what does going deep actually look like in practice? Well, if you pick web development, for example, build three to four complete web applications. Don't just follow one tutorial, right? Now, if you pick data science, same thing. Do five or six end-to-end analysis on real data sets. Repetition on real problems is what builds the skill here, and you need to focus your energy so you get good in that one particular area. The temptation to branch out too early is definitely strong. Make sure you resist it because you're going to thank yourself later for being good in one particular area and actually hireable, especially in the difficult job market we have today. Okay, so now let's move to step five, which is projects, projects, projects. Now, we kind of already touched on this here, but the fastest way to improve is to build things that are slightly above your current skill level. So, follow tutorials to learn a concept, but then immediately build something similar without watching that tutorial. Now, that's where real learning actually happens. You start small, finish everything, and a single completed project will teach you much more than, you know, five half-finished ones that you just never got through. The idea here is that when you work on something that's a little bit more complicated than you currently know, you have to learn something new. You have problems, you have bugs, you have issues that you run into, and you have to actually use your critical thinking skills to find the solution, look up a particular piece of syntax, find a module you need to bring in, and it's not just you watching someone else code or doing something you've done a hundred times before. You're actually challenging yourself, which is personally how I got good at code. Now, in this space, there's a ton of different projects you can build. It really depends on the type of path that you've gone with, but just go look up five most popular Python projects for data science, for web development, whatever, and try to build them, but make sure that you pick something that's beyond your current skill level. And I should clarify that I mean slightly beyond. Don't go pick something super super complex, that's going to be discouraging, but you want to give yourself a challenge. And just while you're at it, make sure that you put all of this stuff on GitHub, so you have it saved for later, and you can access it from different devices. It will also look good on a resume in the future or in your portfolio if you're going to show what you've done. Okay. Now, let's move on to step six, which is the things that nobody tells you. Now, first of all, you want to read other people's code. This is one of the fastest ways to level up that nobody really does or talks about, especially at a beginner level. Go and look at open-source projects on Git GitHub. Go and read through the Python module that you're actually using. I mean, it was written in Python by someone else. And look at how experienced devs are structuring their code and writing things. Now, this isn't going to immediately make you a better developer, but it's going to give you a sense of how people beyond your current skill level are writing code, and definitely give you some things to consider, especially if you look at your code compared to theirs side by side. Next, learn to use the debugger. Now, look, print debugging and print statements work, but learning how to step through your code with a real debugger is a great skill that's going to save you hours of work. Just do this early. Next, write tests and learn about testing. Even just basic tests are going to save you a massive amount of time, and this is something that I personally ignored for years and only learned when I actually needed to get a job, but it really did come back to bite me. Now, pytest makes this super easy. Have a look at that module, you can literally learn it in 30 minutes. Next, learn Git properly. Not just the four or five basic Git commands, but look at how you do merge conflicts, right? Look at how you roll back to previous commits. Look at how you work with Git on a team, how you check the history. The better you know Git, the better off you're going to be, and it's going to save you a massive amount of time if you just have all of the core commands memorized, you know exactly how to work with it and you're not dealing with merge conflicts all of the time. And then lastly, get comfortable with the terminal. You don't need to be a Linux wizard here, but even just understanding basic commands can make you significantly more fluent on the computer, especially now as we're working on all these CLI AI tools, right? Where you should just know how to change directories, make a directory, delete something, you know, execute a file. These are things that are really worth learning that don't take that amount of time. Now look, these skills are not glamorous, right? But they really are the things that separate your hobbyist programmers from professional developers. And personally for me, I can tell immediately when I see someone work if they spent the time to master these skills. Now what I want to leave you with is this. Python is genuinely one of the easiest languages to start with and also one of the most powerful ones that you can master. But just because it's easy to start doesn't mean that it's easy to get good at. The fundamentals take a real time, they take weeks of work, and then to become actually proficient takes months and honestly even years. At this point, I have almost 20,000 hours writing code and I still don't consider myself an absolute expert in any programming language, which is kind of crazy. But the good news here is that the demand for Python developers, especially in AI and data science, has never been higher. The investment absolutely pays off and if I started over today, I'd follow the exact path that I wrote down here. So fundamentals, OOP, pick a direction, go deep, build projects and don't have all of the shortcuts and I would be significantly further than I was today. That's why I'm making this video. If you get one thing from this video, make sure you go deep rather than just going wide. Once you get good in one particular area, you're naturally going to pick up a lot of other skills, but you'll already be ahead of everyone else because you have expertise in this one niche where you can get a job, you can start your life, you can start your employment, and you can start picking up all of those other skills that will naturally come to you. Anyways guys, I hope you enjoyed the video. If you did, leave a like, subscribe, and I'll see you in the next one. >> [music]