This is how I would learn Python if I had to start over. Python is currently the most popular language for data science and AI, and is relatively simple to get started with. It’s flexible and it has a whole bunch of libraries that you can leverage. In this guide, I’m going to give you a complete roadmap to learn Python and get up and ready as fast as possible.
But here’s the catch: because everybody says that it’s simple to get started with, everybody assumes that they can learn it with very little effort. They forget that it’s still a programming language. It requires time and effort, not necessarily to learn the syntax, which is simple, but to understand basic programming concepts and apply them in the real world. That can be very challenging.
📜 History Bit: Python was conceived in the late 1980s by Guido van Rossum in the Netherlands. Its design philosophy emphasizes code readability with its notable use of significant whitespace, a feature that was quite unique at the time.
The Curse of the Self-Taught Coder
As a beginner, you’re going to understand individual concepts like loops, control flow, and functions, but you might struggle to see how they all fit together in a larger program. This was the case for me. The first language that I learned was PHP, and this was back in the day. I learned it superficially at the beginning because I needed to build a website and I relied heavily on a framework. Then, I needed to learn Python, and I learned it at a high level because I needed to do some scraping.
My initial interactions with programming languages were to solve real-world problems. When you do that, you specifically learn the tools that you need in order to get the job done. But that’s not comprehensive, and you’re always going to feel like you don’t know enough. I constantly felt like this throughout the years. Even if I constantly worked on new projects, even if I constantly learned new concepts, libraries, and frameworks, and I got the job done regardless, I always felt like I didn’t know enough. I think this is the curse of being self-taught.
🧠 Neuro-Note: This feeling of “not knowing enough” is related to the Dunning-Kruger effect. To combat it, use project-based learning. Building a complete project forces your brain to connect disparate concepts (like loops and functions) into a coherent mental model, or “schema,” moving you from abstract knowledge to practical application.
If I were to start all over again and learn Python, I would still start by working on real projects because this gives you a clear purpose. But I would do some things differently. The first thing that I would do differently would be to have a clear roadmap that I can follow. This means having a list of concepts that I know I need to cover and refresh before starting to work on a project.
Phase 1: Installation, Setup, and The Basics
Let’s cover these main concepts, starting from the basics to intermediate and advanced topics that are all fundamental. For the basics, you won’t need to revisit them often, but for the intermediate and advanced topics, you need to revisit them multiple times so that they make sense.
First up is installation, setup, and the basics. I recommend that you install Python via the Anaconda distribution. This is the best option if you’re interested in working on data science and AI-related projects because it has all the necessary libraries up and ready. You’ll need to write code using an IDE, and for this, I recommend VS Code. Even learning VS Code properly will require some time so that you get used to the interface, but it’s worth it because this is the standard now.
⚠️ Pitfall: A common mistake is spending too much time customizing your editor (like VS Code) with themes and extensions before writing a single line of code. This is a form of procrastination. Stick to the defaults initially and focus on coding.
Now that you have these, you need to learn about Python variables and naming conventions. Then you can run your first “hello world” script and experiment with Python’s interactive shell.
Now we can start with simple concepts that are the foundation of everything. You get strings, numbers, and booleans. You need to look at string operations and methods, understand string immutability, and advanced formatting. Also, learn about integers and floats, and understand how to perform type conversion.
🔗 Note: String immutability means that once a string is created, it cannot be changed. If you’ve worked with JavaScript, this is similar to how primitive types are handled. Any function that seems to modify a string is actually creating a new one in memory.
Next, you can get into data structures. Understand how lists, dictionaries, tuples, and sets actually work. You need to understand their individual particularities.
I recommend that you write this code yourself. Always type the code; never copy-paste from examples, from ChatGPT, or from Stack Overflow. You need to always write every character. Don’t use auto-formatting or co-pilot because you’re learning the basics. It’s important to take your time, as you do when you’re first learning how to write. You focus a lot on calligraphy, right? You need to establish good patterns straight from the start, and the mind has time to grasp all of these concepts. It’s the same with coding.
🧠 Neuro-Note: This is called kinesthetic learning. The physical act of typing the code yourself activates motor pathways in your brain, creating a stronger “muscle memory” for syntax and structure than passive reading or copy-pasting ever could.
Coming back to data structures, look into nested data structures, both lists and dictionaries. We’re going to work a lot with these, so you need to understand how they work. Now’s the time to learn control flow: if-elif-else statements, nested statements, and ternary operators for writing more concise statements. Also, for and while loops are super important to understand, and both are very straightforward.
The last basic concepts that you need are list and dictionary comprehensions. These look beautiful and you’re going to use them a lot. You can go through all of these concepts in maybe a day or two. These are just the basics, but even for these basics, you need to revisit them every now and then.
Phase 2: Building on the Foundation
Now you have the habit of writing code yourself, so keep going. Again, don’t copy-paste code or use autocomplete or auto-format. If you make a mistake and the code doesn’t work, it’s most probably a misspelling, some bad indentation, or something simple.
Next, you can start looking at Python functions. You’ve got to learn how to write basic functions with parameters and return values. This will also lead you to understand variable scope and variable lifetime. If your code doesn’t work, it’s most probably because you’re not closing a parenthesis or you have some bad indentation. But you’ve got to keep writing it yourself. You need that practice in order to form your coding memory.
⚠️ Pitfall: A common beginner mistake with functions is writing a single function that does too many things. A good function, like a good tool, should have one clear purpose (the Single Responsibility Principle). This makes your code easier to debug and reuse.
Now is also the best time to learn about lambda functions, their syntax, and the use cases where they’re best suited. Also, you need to look into how to read from and write to files.
By now, you’ve covered a lot of ground in terms of basics and you’ve made errors along the way. So, it’s time to learn how to debug. You need to look into reading and interpreting error messages and also use breakpoints to perform step-through debugging. You also need to handle type errors and exceptions using try-except-else-finally statements. Learn about common debugging strategies and best practices. This is important because you’re going to do this a lot, especially when you’re using a lot of GPT-created code.
📜 History Bit: The term “debugging” was popularized by Grace Hopper in the 1940s. When an early computer, the Mark II, was malfunctioning, her team investigated and found a moth trapped in a relay, literally a “bug” in the system. They removed it and taped it into their logbook.
The last foundational topics for learning Python are comments and docstrings. Writing effective comments alongside your code and docstrings for documentation is crucial in order to remember what you did and also for others to know what you did when they review your code. It might feel time-consuming, that you can do it later, but believe me, if you don’t comment your code immediately and create the docstrings for your functions, you’re going to forget. Two months down the line, you’re going to come back to your spaghetti code and you’re going to understand nothing from it.
Up until now, you’ve probably spent a week or so learning these. You can go ahead and tell your friends that you learned Python in a week, and it’s good to believe that you did, but deep down, you’re going to know differently. So you need to continue.
Phase 3: Practical and Professional Skills
You can continue with the following: you need to understand Python virtual environments. You won’t always be working in your base Anaconda environment. You need to learn why you need them and how to create and manage them. Look into venv, pipenv, and conda environments. They serve similar purposes, but you’ve got to find out the differences. Also, learn about best practices when it comes to working with different project dependencies.
🧠 Neuro-Note: Using virtual environments is a form of compartmentalization. By isolating project dependencies, you reduce your cognitive load. Your brain doesn’t have to worry about conflicting package versions between projects, allowing you to focus entirely on the task at hand.
Now that you’ve learned a lot, you need to learn a little bit more. By this, I mean Python modules and libraries, specifically the standard Python libraries. You’re going to use math, datetime, random, os, sys, re (for regular expressions), subprocess, urllib, and a lot more that you can see here. Cover these ones in detail so that you know exactly what you can leverage when you actually need it.
🔗 Note: Think of the Python Standard Library as a built-in set of Swiss Army knives. Before you look for an external package to solve a problem (like handling dates or files), always check if there’s a standard library module that already does it.
Now we only have one big chapter that you need to dive very, very deep into. This is something that you’re going to be using all of the time in your professional career: APIs. Specifically, RESTful APIs and HTTP methods such as GET, POST, PUT, and DELETE. You’ve got to look into the structure of an API request and response. You can work with a tool like Postman; it will definitely help you a lot. You’ve got to learn how to work with JSON data and send authenticated requests.
📜 History Bit: The concept of an API (Application Programming Interface) is older than the web itself. Early APIs were simply libraries of functions for operating systems. The modern web API, driven by HTTP, was formalized by Roy Fielding in his 2000 dissertation, which defined the principles of REST (Representational State Transfer).
You need to spend as much time as needed here. You should start using APIs, for example, such as OpenAI’s API. If you do so, you’re also going to be able to call yourself an AI Engineer. But really, working with APIs will also give you the ability to start working on your own projects where you can leverage data from providers.
Your Journey Begins Now
This is the complete roadmap that you need to follow in order to start your Python journey. With a little bit of dedication, I think you can cover all of these concepts in about two weeks. But after these two weeks, I think you’re going to be ready to start working on a real project, knowing that you’re equipped with a broad understanding of Python.