Do You Really Need to Know How to Code? Exploring the Role of No-Code in Data Science

Earlier this week, I was chatting with Kate about coding. She mentioned her experience, and I went on to share that it’s something that I teach myself sporadically. I use an app called Mimo. It makes learning code so easy — something I can do when I have idle time (which is rare). I also mentioned that I sometimes question whether learning code is really necessary at this point with the rise of no-code and low-code tools.

With a bit of research, I discovered the answer isn’t that simple. Coding is still valuable, but it isn’t always essential. According to an article I read about the topic, Gartner’s Magic Quadrant predicts that, the no-code/low-code market is set to grow by 23% in 2024. These tools are everywhere now, letting people build data pipelines, visualize data, and create predictive models without writing code.

Why Coding Still Has an Advantage

No-code tools make it easier to work with data without writing code. With platforms like DataRobot and Google AutoML, even those who don’t know programming can set up data models, clean data, and make predictions. This approach is fast and user-friendly, which is why many businesses are adopting it to get quick insights.

But these tools do have limits. For example, with a no-code tool, I could make a simple recommendation model, like suggesting movies on Netflix or Amazon Prime based on past choices. But if I wanted a smarter system that updates in real-time and adjusts to personal tastes, I’d need to know Python and SQL. Python would help me create models with tools like Pandas and Scikit-Learn. SQL would let me organize and pull data from databases.

Coding also opens more job opportunities. Many employers want people who can code because they can handle more tasks. So, while no-code tools are helpful, coding still adds real value in data science.

The Balance: No-Code for Speed, Code for Depth

So, what’s the verdict? A mix of no-code and coding skills works best. No-code tools are fast for simple insights. But coding allows deeper analysis and more flexibility. Beginners can start with no-code to build confidence. Then, they can learn Python or SQL for more depth. Experienced data leaders who know both can handle different projects.

What do you think — will coding still be necessary in the future of data science?