There are lots of great reasons to learn Python, but most people quit before they've reached their learning goals in this video. I'M gon na show you a five-step approach that has worked for thousands and thousands of learners, including me, and I think, it'll work for you too, to start with. Yes, I work at data quest an online platform that teaches Python and other data science skills, but this video isn't a sales pitch and this approach can work for anyone, regardless of why you want to learn Python and what platform you're learning with step. One is something: you've probably already done, find your motivation to learn Python, but it's worth taking the time to sit back and really reflect on that. Why do you want to learn Python learning any programming language is gon na, be a challenging and sometimes very frustrating journey. If you don't have a good reason to do it, it's gon na be really easy to quit.
Ideally, your motivation should be something that's specific and project-based. For example, I want to learn Python to build my idea for a video game, or I want to learn Python, to build models to predict the stock market. That gives you a clear goal. It narrows down the list of things you need to learn and it allows you to work incrementally. If you have a vague motivation like I want to learn Python, to make more money, you won't be able to make incremental progress toward it. Your salary isn't gon na creep up slightly every time you learn a new thing and when you have to wait until the end of a really long journey to get any kind of award, it gets really easy to. Let yourself give up
I know because I've done that myself having a vague goal, also doesn't help you focus on what specific aspects of Python you need to learn. What you need to know to use Python for data science, for example, is pretty different from what you need to know to use Python to build a game. So if you don't know what you want to do with Python, you're gon na have a hard time, even figuring out what you need to learn once you get past the basics. Speaking of the basics, step 2 is learn basic Python, syntax,
The key here - and this is really important - spend as little time learning this as possible. You don't need to learn everything. You don't need to become a Python syntax master. You just need to learn enough to move on to the next step, aim to spend no more than a few weeks and that's assuming you're only doing part-time self-study, because it's really not very interesting. Your goal should be to work quickly through the basics and get to a point where you can working on projects as soon as possible and that's step. Three start working on structured projects based on your motivation. Working on projects is really valuable. They'Ll help you build real world experience, while forcing you to practice what you've learned and forcing you to learn new things and confront new challenges and, at the same time, it's much easier to stay, interested and motivated when you're working on a project, because you're focused on A topic that interests you and you're working towards building something real
For example, if you're interested in learning Python for data science once you've got the basics of Python, syntax under your belt find a tutorial analyzing, A data set that interests you or grab your own data and start trying to work with it using Python. This process can be painful, but because you're actually interested in what you're doing you're much more likely to stick with it check out the links in the video description below for lots of project resources or just scroll down. If you're seeing this video on the data quest website, step four is to break away from the structure of a tutorial or a guide and start building projects totally on your own, particularly at first
This will be a challenge: that'll, send you to Google and Stack Overflow Quite a bit, but that challenge is gon na help. You learn a lot of new things. If your project idea seems too difficult at first try breaking it down into smaller sections and approaching it step by step. Also remember that resorting to Google is not a failure. Every time you encounter a problem and have to look up the answer. You'Ve learned something and the dirty little secret about programming is that even the pros go to Google for answers all the time. Step 5 is to keep working on progressively harder projects that might sound the same as step 4, and it's certainly a continuation of that. But the progressively harder part is really key When you're able to build your own projects that can become really tempting to keep building similar things. That gets you the satisfaction of completing a project over and over again, but it also keeps you within your comfort zone, to keep getting better
You need to up the difficulty on yourself whenever you find you're getting complacent, you can do that with a new project or by trying to build and add complexity to an existing project. If you need to add some difficulty to a project, you've already built here, are a few ideas. Try writing a tutorial about how to build your project, teaching other people about how and why your project works can be a real chance and it'll highlight areas of weakness. Of your understanding, can you scale up your project to handle more data or more users more traffic? Can you make your code faster, more efficient? Can you make your project useful for a bigger audience? Can you make it production ready or even turn it into a real product? Can you make it more self-contained by eliminating some of the dependencies learning? Python is kind of like learning a spoken language, there's always more to learn and there's really no end point, but at some point is you're.