Python Day #22: Python Career Guide - What’s Next After Learning Python?
Discover the best next steps after learning Python in 2025. Explore web apps, data analytics, machine learning, and automation to grow skills and boost your career.
When you first started learning Python, it probably felt like stepping into a whole new world. I say this because that is how I felt…
Variables, loops, and functions seemed all so foreign, written by only the smartest who sat in front of glowing computer screens.
But after putting in the time through practice, building projects, and working with libraries like PyGame and PyQt you’ve now hit an important milestone. You now understand Python’s core tools and can use them with more confidence.
So the big question is: what comes next? A question I get asked all the time!
This is the final article in our series, The Python Roadmap to Success.
Every week you’ll be introduced to a new topic in Python, think of this as a mini starter course to get you going and allow you to have a structured roadmap that actually builds to create you a solid foundation in Python. Join us today!
My goal all along has been to give you more than just theory. I’ve been writing to give you a clear, practical path you can actually follow. And now that you’ve made it this far, it’s time to move beyond the basics and start specializing.
In fact, throughout this article I am going to link courses I’ve built to get you started in any of these niches. Most of them are free, on my YouTube Channel!
Python is a language that can be used in so many fields, from web development to artificial intelligence, but you can’t learn everything by only scratching the surface.
The final step is all about focused exploration: pick one area, spend a month or two really digging into it, build real projects, and then decide whether you want to keep going deeper or shift to something new.
To make that choice a little easier, I want to highlight four strong paths you can take: Web Applications & APIs, Data Analytics, Machine Learning, and Automation & Web Scraping.
Each of these areas builds on what you’ve already learned in my Python Roadmap, they connect to real-world problems, and opens doors to future career opportunities.
One point I should note is, spend time digesting these when I write them, after six weeks most of my posts are automatically put behind a paywall.
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The Power of Experimentation
One of the biggest mistakes you can make after learning the basics is jumping from one tutorial to the next without ever settling on a focus. This is tutorial hell, we need to get you away from this!
It’s like trying to learn ten languages at the same time, you might pick up a few phrases with each, but you’ll never get good at any of them.
A better approach is to pick one area that really excites you—whether it’s web development, data analytics, machine learning, or automation the commit to it for a month or two.
So ask yourself, “What do I have an interest and passion for?”
Treat this next step like a personal internship. Don’t just copy what tutorials show you! Start building real projects and solving real problems. That’s when you begin to see real progress and feel the shift from learning to creating.
Learn Python. Build Projects. Get Confident!
Most people get stuck before they even start…
Wasted hours Googling, watching random YouTube videos, and never actually finish a project.
But that doesn’t have to be you!
The Python Masterclass is designed to take you from “I don’t know where to start” to “I can build real-world Python projects” — in less than 90 days.
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Here’s what you’ll get when you join today:
✅ All Course Videos — Learn Python without wasting time on scattered tutorials
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My masterclass is designed so you see your first win in less than 7 days — you’ll build your first working Python scripts in week one and finish projects in your first month.
The sooner you start, the sooner you’ll have projects you can actually show to employers or clients.
Imagine where you’ll be 90 days from now if you start today.
👉 Ready to get started?
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Web Applications & APIs: Building for the Online World
Think about the way you use the internet every day. You sign into a website, order something online, stream a show, or check the forecast on your phone. Behind all of these things is a web application, built and powered by code.
Python has become one of the most popular tools for this job, especially with frameworks like Flask and Django. Learning them is kind of like being both the designer and the builder of digital spaces.
If you are more visual, then this is a great path to pursue. You get real feedback and can actually watch your creations come to life!
You may have seen Flask before, maybe not. A simple setup looks like some of the code I have below.
This tiny program creates a basic web app. When someone visits the right page, it replies with a simple message. From here, you could add databases, user logins, or even build an API that other developers can use in their own projects.
👉 Get Started with Flask Today - Intro to Flask Full Course
The best part about this area is how practical it is. Everyone uses web apps, so learning to build them connects your code directly to everyday life. You’re not just writing programs—you’re shaping tools that people interact with every single day.
Databases and SQL in Web Apps
Almost every web app needs a place to store information—things like accounts, posts, or products. That’s where databases come in. Pairing Python with SQL gives you the power to build apps that don’t just show information but can save it, update it, and let users interact with it.
Not only do you learn Flask and Django in Web Dev, but you’ll inevitably learn new tools that are used heavily in Python. This is where SQL and ORM’s come into play.
👉 I genuinely hope you get value from these articles, if you do, please help me out, leave it a ❤️, and share it with others who would enjoy this. Thank you so much!
Data Analytics – Turning Numbers into Insights
If building web apps is like putting up houses, then data analytics is more like digging into data. You start with a pile of raw numbers, sort through them, and slowly uncover patterns and stories that were buried inside.
Every company, whether it’s a small coffee shop or a global giant needs people who can do this. The insights pulled from data help guide big decisions, shape strategies, and even predict the future.
Python makes this process straightforward with libraries like Pandas, Matplotlib, and Plotly. For example, imagine you’re looking at sales data for a coffee shop. With just a few lines of code, you can spot trends:
In just a few steps, you’ve taken a messy spreadsheet and turned it into a chart that’s easy to understand. That’s what data analytics is really about. It’s not about showing off how technical you are—it’s about turning numbers into clear, useful insights that anyone can act on.
👉 Get Started with Data Analytics - My Data Analytics Playlist Here
What makes this path exciting is how universal it is. Sports teams use data analytics to evaluate players. Hospitals use it to improve patient care. Marketers use it to understand customers. Banks use it to predict risks. So my point is, everyone uses and needs this.
The Power of Data Visualization
Numbers alone can be tough to digest. That’s where visualization comes in. With the right chart, you can take a wall of confusing data and make it clear in seconds. Matplotlib helps you build classic graphs, while Plotly lets you create interactive dashboards people can actually click through and explore.
If you choose this path you are going to learn real tools like: Pandas, Polars, MatplotLib, Seaborn, and Plotly. These are just a few, but you can do so much using them.
If you’ve been following along with this roadmap, the Masterclass is where you take that next leap. You’ll not only build PyQt apps step by step with me, but also strengthen your Python skills in ways you just don’t get from books or tutorials.
Enrollment is open now, so if you’re ready to turn your Python knowledge into real-world coding ability, join the Masterclass and see how far you can go.
P.S - Save 20% off your First month with code: save20now at checkout!
Machine Learning – Teaching Python to Learn
Now we come to one of the most exciting areas of Python: machine learning. If data analytics helps us understand the past and present, machine learning takes things a step further by predicting what comes next. It’s about teaching computers to spot patterns, make decisions, and even improve as they go.
Think about how Netflix seems to know exactly what you’ll want to watch, or how Spotify builds a playlist that matches your mood. These aren’t lucky guesses, they’re the result of machine learning models trained on your past choices.
With Python libraries like Scikit-learn and TensorFlow, creating these kinds of systems is possible even for beginners.
In just a few lines of code, you’re teaching the computer: “Here are some houses, their sizes, number of bedrooms, and what they sold for. Learn the relationship between those things.
Now, predict the price of a 2,000-square-foot, three-bedroom house.” That’s the core idea of machine learning—computers learning from data to make predictions.
Of course, this field can get much deeper. You’ll eventually hear about neural networks, reinforcement learning, or natural language processing. These topics can take months to really understand.
But even starting small, the experience is eye-opening. You’re no longer just writing instructions for the computer—you’re building a system that can adjust, adapt, and teach itself.
👉 Get Started with Machine Learning Here
The biggest key to success here is patience. Machine learning can feel intimidating, but the best way forward is to begin with simple projects. Try building a program that spots spam emails or predicts basic stock price trends.
As you get more comfortable, take on more advanced challenges. Few things in programming compare to the moment your model makes a correct prediction—it feels almost magical.
Deep Learning with TensorFlow or PyTorch
When you’re ready to dive deeper, you’ll come across deep learning. Instead of just drawing lines to fit data, deep learning uses neural networks, these are systems inspired by how the brain works. These models can recognize images, understand text, or even help drive cars.
For instance, with TensorFlow, you can train a model to recognize handwritten digits from the famous MNIST dataset. It’s a simple project, but it gives you a glimpse into how computers can “see” the world.
From there, the possibilities only grow—image recognition, voice assistants, language translation, and much more all stem from this branch of machine learning.
👉 I genuinely hope you get value from these articles, if you do, please help me out, leave it a ❤️, and share it with others who would enjoy this. Thank you so much!
Automation & Web Scraping – Putting Python to Work
The last path we’ll look at is automation and web scraping. This area is all about saving time and making your work easier. If building web apps is like constructing digital houses, and machine learning is like creating digital brains, then automation is like hiring a reliable assistant who never gets tired.
It takes care of the boring, repetitive stuff so you can focus on the things that actually matter. Automation is a huge reason people are using Python too.
Think about the small tasks that eat up your day—downloading the same reports over and over, sending out routine emails, or copying information from a website. With Python, you don’t have to do those by hand anymore.
Tools like Selenium and BeautifulSoup let you pull data straight from web pages, while Python’s built-in libraries can handle things like managing files or sending emails automatically.
In just a few lines, you’ve grabbed the title of a webpage without ever opening a browser. Scale this up, and you can gather entire datasets from the internet—data that can later be used in analytics or machine learning projects.
The beauty of automation is how quickly it pays off. You don’t need months of work to see results. Even a short script that saves you a few minutes every day adds up over time.
The more you experiment, the more you’ll feel like you’ve built a set of personal helpers working in the background. Spend just a month exploring automation, and you’ll see how powerful Python can be in making everyday life more efficient.
👉 I genuinely hope you get value from these articles, if you do, please help me out, leave it a ❤️, and share it with others who would enjoy this. Thank you so much!
Choosing Your Path and Sticking with It
Now that we’ve walked through these four areas, the real question is: where should you start? The truth is, all of them are useful, and many devs eventually explore more than one.
But right now, the best move is to pick a single path and stick with it for at least a month or two. Use that time to build projects that push you past the basics and give you real experience.
If you commit to just one and practice regularly, you’ll start to develop real skill and confidence. Once you’ve made progress in one area, you can always circle back and try another.
A good way to choose is to think about what excites you most. If you enjoy creating things that people can actually use, like websites or apps, then web development is a natural starting point. If you’re fascinated by finding patterns in numbers and love turning data into visuals, then data analytics might be the right fit.
If you’re drawn to new tech like self-driving cars or artificial intelligence, machine learning is the way to go. And if you care most about saving time and making life more efficient, automation and web scraping will feel incredibly rewarding.
The important part isn’t which path you pick first, it’s that you commit to one long enough to build real projects and gain real confidence.
That’s how you turn curiosity into skill Python nerds.
My Best Starter Resources
Here are the best resources I have to offer to get you started with Python no matter your background! Check these out as they’re bound to maximize your growth in the field.
Code with Josh: This is my YouTube channel where I post videos every week designed to help break things down and help you grow.
Zero to Knowing: My Python Masterclass Subscription gives you everything you need to go from zero to building real-world projects — with new lessons, challenges, and support every month. Over 1,500+ students have already used this exact system to learn faster, stay motivated, and actually finish what they start.
My Books: Maybe you’re looking to get a bit more advanced in Python. I’ve written 3 books to help with that, from Data Analytics, to SQL all the way to Machine Learning.
Wrapping Up Lesson Twenty-Two
Learning Python doesn’t stop once you understand loops, classes, or the basics of syntax. That’s just the starting point.
Real growth comes when you take what you know and use it to solve problems that matter to you, build projects that push your limits, and explore areas that spark your curiosity.
From building web apps, diving into data, teaching machines to learn, or automating everyday tasks, the secret is staying consistent and focused.
Throughout this series, we’ve moved from the fundamentals of Python to object-oriented programming, and now to the different directions you can take next.
My hope is that you’ve gained more than just technical know-how—that you’ve also started to picture how Python can fit into your future.
The road ahead will have its share of trial and error, but it will also bring discoveries and wins that make the effort worth it.
So, what’s next?
The real answer doesn’t come from Python itself, it comes from you. Python is only a tool, and its value depends on what you decide to create with it.
Pick a direction, commit to it, and keep building.
The journey doesn’t end here, it’s just getting started.
Hope you all have an amazing week nerds ~ Josh (Chief Nerd Officer 🤓)
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