How to Improve Your Python Skills

In this article, I’ll show you how you can improve your Python skills. Discover what you can do better, faster, and more effectively as you learn Python like never before!

You’re all set! You started to learn Python, and your Python home office is ready. But you wonder what to do after completing your first Python course. Or maybe you want to improve your Python skills and increase your market value.

One thing you will need to do, whether you are a beginner or a seasoned Python developer, is to keep becoming more skilled. Doing so will help you stay relevant and increase your employment value. So, let’s see how and why you should build you Python skills.

Why Learn Python, Anyway?

Knowing Python will offer you more opportunities, but you have to keep writing better Python code as time goes on. If it seems like your motivation is fading because there’s so much work to do, read Kateryna’s article on why you should learn Python in 2022. And if it’s not enough, an overview of Python developer salaries might help. Spoiler alert: Python jobs are pretty sweet!

An excellent way to increase your value in the marketplace is to specialize in data science. Data-powered applications are transforming every single industry, so there are lots of opportunities. If you are excited to get started, look no further! We have a Python for Data Science track to help you achieve your goal. These 5 courses will lay the Python programming foundations you need to work with data.

Ok, let’s move on to improving your Python skills outside of an online course.

5 Ways to Improve Your Python Skill

1.   Master the Syntax

The first thing you must do is master Python’s syntax; it will help you be a faster developer. You’ll search less for the proper commands, get fewer errors, and have better code execution, which means that you will save time. And, in a programming interview, this will help you solve more exercises.

This point brings me to the next one. To achieve syntax mastery, you have no choice but to code every day.

2.   Code Daily

Coding every day is a must, but you do not have to spend three hours a day on it. A short, well-planned session of algorithm exercises can work wonders.

The idea is similar to repeating technical exercises for a sport, musical instrument, or any other learned skill. Boxers practice shadow boxing to improve their boxing flow; runners practice exercises to improve their stride; chess players solve puzzles to be better at finding competitive tactics. Similarly, Python developers need to solve puzzles to find faster and better-optimized programming solutions.

How do you do an algorithm exercise? Set your clock for 20 minutes; of those, spend 10 minutes to look at the solution and understand it. The latter brings us to the next point: the importance of reading others’ Python code.

3.   Read

Reading is another way to quickly improve your Python skills. My advice is to read two things: code from more advanced users and Python books.

Read Others’ Code

You will stall quickly if you do not learn from others. This is why it is essential to look at the solutions of Python coding puzzles, Python libraries’ code implementations, and various Open Source projects.

You can find a ton of resources on GitHub and StackOverflow. If you find an exciting project on GitHub, clone it and take the time to understand it line by line. Feel free to play with it as well!

Read Python Books

Having a solid grasp of the theory behind programming and Python will do wonders for your coding skills. Like chess, you cannot be great at programming without understanding the theory. Building the daily discipline to read Python books will lead you far.

If you need some suggestions, here is a list of the best Python books and another of the best Python books for data science.

4.   Pair Program

Another way to improve your Python skills is to find a learning partner. It can be a friend who is also learning to program or is building a project. Meeting up to code can help you keep going if you find it challenging to stay motivated by yourself.

It can also be an opportunity to team up to work on a joint project. If you’re into data science, why not create a team to compete on Kaggle? It would be a great way to add some interaction to your coding sessions, gain experience, and add a new project to your portfolio.

5.   Code Your Own Project

While solving puzzles gives you coding sessions, sessions alone are not enough. You must build projects to gain experience, enrich your portfolio, and be able to add marketable skills to your resume.

But what can you build with Python? This article provides interesting insights on what you can create with Python.

In selecting your projects, be specific  and don’t reinvent the wheel.

By “be specific”, I mean you should choose projects that will help you professionally. If you want to be a backend developer, building an API can be a good project. If you want to be a computer vision engineer, a project related to object detection will be more relevant. You do not need to have ten projects in your portfolio; 3 to 5 well-targeted projects are enough.

Second, do not code everything from scratch or you will be working on your project for the next two years. We stand on the shoulders of giants, and you should take advantage of this. This point is related to imposter syndrome, where you feel that you are never good enough to land a developer job. I think this is especially true if you’re making a mid-career switch like I did.

You’ll be working alone, so do not try to build a whole-team project. Instead, illustrate a specific concept. For a data science project, you could try to find the best model possible from scikit-learn. Or you could build a linear regression/logistic regression model from scratch, explaining in depth the statistics behind it; eventually, you could fit a linear regression from scikit-learn to compare with your own implementation. You could also contribute to open-source projects or try reimplementing a research paper.

You can find some data science project ideas here. If you haven’t started to study data science yet, check out our Python for Data Science Course.

Should You Learn Another Programming Language?

You might be tempted, at some point, to learn another programming language as a way of increasing your Python skills. I advise you not to do it; you will waste your time. It is essential to understand the difference between learning a programming language and learning how to program. If you are a beginner, you should focus on learning how to program. At this point, Python is only a means to an end (but quite an awesome one, if you ask me!)

If you decide to go ahead with another programming language, you will relearn how to do the same things you already know in Python. It is similar to martial arts; let’s say you studied karate for a year and then decided to also train in judo. While you spend time learning the basics of judo, you are slowing down your progress in karate; you would probably be a better martial artist by sticking to karate for now. Exploring judo once you master karate would help you round out your training.

However, if you’re into data science, there’s an exception to this rule: you can explore SQL as you learn Python. It’s very common for data scientists to use both languages. If you do not know where to start, you can learn SQL basics here.

Improve Your Python Skills Today!

In this article, I shared some of my best tips to help you improve your Python skills. You can write better code through courses, coding puzzles, books, and projects. It’s also a good idea to watch lectures, attend webinars, and listen regularly to Python podcasts.

You can also find many interesting resources at LearnPython.com; if you haven’t yet, check our Python basics course to help you get started. The great news is that the first course in this track (Python Basics. Part 1) is completely free at the time of writing! That’s 95 interactive Python exercises you can do for free! Just log in or create a free account and start learning Python. This is a great first step into the IT world and a good way to find out if programming is for you.