Do I Need to Learn MicroPython as a Data Scientist?

Checking out StackOverflow’s 2024 survey, over 65,000 developers responded about their experience with coding and the technologies and tools they use and want to learn. One of the things that caught my attention was MicroPython.

The chart in the link above shows that 1.8% desired the language, whereas 48.6% admired it. We all know what that means. Programmers do not admire a language for no reason. The language must improve the employee’s workflow.

This article will review everything you need to know about MicroPython.

What is MicroPython?

 
Many will assume it’s just a micro version of Python, one of the most popular programming languages. And yes, you are right. MicroPython is a lean and efficient implementation of the Python 3 programming language, including a small subset of the Python standard library. It is optimised to run on microcontrollers and in constrained environments.

Let’s break it down a little bit more.

• Micro version of Python: Designed to use less memory and computing power in comparison to Python. This means the language is perfect for devices with a few kilobytes of RAM.
• Microcontrollers: The way you want to see this is a microcontroller is a tiny computer on a chip which can control smart home devices, robots, etc.
• Constrained environments: Low resources systems that typically have little memory and computing power.

So, what is the purpose of MicroPython?

If you already know the Python programming language, MicroPython allows you to program hardware without learning a new language, such as C++. Yes, C++ has its advantages, but if you are purely looking into creating a prototype with the least amount of effort possible, MicroPython is great precisely for that.

Should I Learn MicroPython as a Data Scientist?

 
Looking at the data from StackOverflow’s survey, to simply put it, I would say yes. If the developer community is interested in it and shows their desire for it and their admiration, why wouldn’t you?

Here are a few use cases of MicroPython:

• IoT: From controlling smart home devices or controlling sensor data for dashboards.
• Edge computing: Running machine learning models directly on edge devices, from your smartphone to your camera.
• Prototyping: If you have Python knowledge, you can quickly set up a prototype for a hardware project.
• Robotics: A big aspect of the AI space which everybody has their eyes and ears on. You can use MicroPython to control motors or sensors in robotics projects.

As AI becomes essential to a data scientist’s roles and responsibilities, MicroPython can serve as the bridge between hardware and software. Allowing you to collect and process sensor data within your data science or machine learning pipeline.

You can run simple AI models directly on your computer without depending on the cloud. IoT and edge computing play important roles in the future of data science projects, so MicroPython should be part of your continuous learning journey.

Wrapping Up

 
Have you used MicroPython? If you have, what have your experiences been, and would you recommend it to fellow data scientists? Let us know in the comments. We’re here to help everybody grow!

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.