To gather insights, I used Indeed.com’s job trend search feature. These statistics reflect how frequently certain skills appear in job postings over time, indicating what employers are looking for. However, it's important to note that this isn’t a survey of which skills are most valuable, but rather a trend showing how the popularity of specific skills has evolved. It’s more like the “first derivative” of skill prevalence — essentially, it shows how quickly interest in a particular skill is rising or falling.

Let’s take a look at the data. I searched for job listings that combined the terms “machine learning” and “data science,” focusing on the most commonly mentioned programming languages. The top ones included Java, C, C++, and JavaScript, along with Python and R, which are widely used in the field. I also added Scala and Julia, which are gaining traction due to their performance and modern design. Here are the results:

When we narrow the focus to just “machine learning,” the trend looks similar, as shown below:

So, what can we conclude from these trends?

First, the chart clearly shows that multiple languages are popular, making it hard to pinpoint a single “best” option. Second, the overall rise in demand for these languages suggests growing interest in machine learning and data science over the past few years. Third, Python stands out as the leader, followed closely by Java and R. Interestingly, Python is not only ahead of Java but is widening the gap, while Java and R are becoming closer in popularity. I was surprised to see Java in second place, as I had expected R to be higher.

Fourth, the growth of Scala is impressive. A few years ago, it was almost unheard of in this space, but now it’s competing with more established languages. This is even more evident when looking at the actual numbers from Indeed.com:

Fifth, while Julia still has a relatively small presence, its popularity has been increasing steadily in recent months. Will it become a major player in the future? Time will tell. If we remove Scala and Julia from the view, Python and R clearly show the strongest growth compared to other languages.

Looking at this curve, it seems possible that R could soon overtake Java in popularity. However, when we switch the query to “deep learning,” the results change significantly:

In this case, Python remains the top choice, but the ranking shifts: C++ comes in second, followed by Java and C. R drops to fifth place. This highlights that high-performance computing languages are more relevant in deep learning. While Python leads, Java is quickly catching up, and R may not reach the top anytime soon. Surprisingly, Lua — the main language behind the Torch framework — is not even listed, nor is Julia.

So, to answer the original question: Python, Java, and R are currently the most popular languages in machine learning and data science. If your focus is on deep learning, then C++ becomes more important, followed by C. However, keep in mind that this is just one perspective. If you're aiming for an academic career or just learning for fun, you might get different recommendations.

In my own experience, I wrote about this earlier this year. Beyond supporting many mainstream machine learning frameworks, Python fits well with my background in computer science. I also enjoy developing new algorithms in C++ because of my long-term experience with it. But again, this is personal. Others with different backgrounds may find a different language more suitable. For example, statisticians with limited coding experience might prefer R, while strong Java developers may stick with Java due to its extensive open-source libraries.

Ultimately, my advice is to read various perspectives on this topic and invest time in learning a language that aligns with your goals. There’s no one-size-fits-all answer, but understanding the trends can help guide your decision.

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