To gather insights, I analyzed trends from Indeed.com, which provides search frequency data for job postings over time. These numbers reflect what employers are looking for, not a survey of skill importance. However, they do show how the popularity of certain skills has evolved. It's more like a "first derivative" of skill prevalence—capturing the rate of change in job requirements, including skills added, removed, or updated.

Let’s look at the data. I searched for job titles related to “machine learning” and “data science,” focusing on the most commonly mentioned programming languages. The top contenders were Java, C, C++, JavaScript, Python, R, Scala (used with Spark), and Julia, which is often considered a next-generation language for data science.

When we focus specifically on machine learning, the trend looks similar, as shown below:

So, what can we conclude from these trends?

First, several programming languages are gaining traction in the field, making it hard to pinpoint a single “best” choice. Second, the overall growth in interest in machine learning and data science is clear—more and more companies are hiring for these roles.

Third, Python stands out as the leader, followed closely by Java, then R and C++. Python has overtaken Java and continues to widen the gap, while Java and R are getting closer. I was surprised to see Java in second place—I had expected R to be higher. Fourth, Scala shows impressive growth, having almost no presence three years ago but now competing with established languages.

This trend becomes even clearer when looking at actual data from Indeed.com:

Fifth, Julia isn’t yet widely used, but its popularity is growing. Whether it will become a major player in the future remains to be seen. If we remove Scala and Julia from the chart, Python and R clearly lead in growth compared to other languages.

Looking at the curve, it seems possible that R might soon overtake Java. When we switch the query to “deep learning,” the results are quite different.

In this case, Python still leads, but the rankings shift—C++ comes in second, followed by Java and C. R drops to fifth. This highlights the importance of high-performance computing languages in deep learning. For general machine learning, Java is quickly rising in popularity, while R may take longer to reach the top. Surprisingly, Lua, the main language for the Torch framework, isn't even on the list, nor is Julia.

So, to answer the original question: Python, Java, and R are currently the most sought-after languages in machine learning and data science. If your focus is on deep learning, C++ should be your priority, followed by C. But keep in mind that this is just one perspective. If you're aiming for an academic career or just want to learn for fun, you might get a different answer.

Personally, I’ve written about this topic before. Python is my go-to language because it supports many mainstream frameworks, and I have a computer science background. I also enjoy developing algorithms in C++ due to my long-term experience with it. But again, this is just my personal situation. Others may find a different language more suitable based on their background. For example, statisticians with limited coding experience might prefer R, while strong Java developers may continue using Java, given its extensive open-source libraries.

In short, my advice is to read various perspectives and then commit to learning a language that aligns with your goals and interests. There's no one-size-fits-all solution, but with the right approach, you can make an informed decision.

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