What Is the Most Popular Language for Machine Learning?

Which programming language should you master to get a job in machine learning or data science? This is a silver bullet problem. This issue is debated in many forums. I will provide my own answers and explain why in this article, but let's look at some data first. After all, practitioners of machine learning and data science should remember this: there is no voice without investigation.

Now, let's look at some data. We use the trend search provided by indeed.com to extract statistics. These data search the frequency of specific jobs over time from the recruitment information, indicating which talents with what skills employers are looking for. However, please note that this is not a folk survey on which skills are most useful, but a trend reflecting how the popularity of job skills evolves (more specifically, it may be close to the first derivative of the popularity of job skills, because the latter is the result of the skills listed in the recruitment information plus job retraining skills minus retirement and resignation skills.)

Well, let's look at the data. I searched for skills combined with "machine learning" and "data science", among which the more prominent programming languages are Java, C , C and JavaScript. I also added popular languages for machine learning and data science like Scala to spark, Python and R -- and Julia, which is considered to be the next generation of popular languages. After inquiry, we get the results as shown in the figure below.

When we only focus on machine learning, we get similar data, as shown in the figure below.

What conclusions can we draw from these data?

First of all, we can see from the chart that it is difficult for everyone to adjust. In this case, many languages are quite popular.

Secondly, the popularity of all these languages is soaring, which shows that people's interest in machine learning and data science is increasing in the past few years.

Third, Python is obviously ahead, followed by Java, R and C . Python surpasses Java and gradually opens the gap. At the same time, the gap between Java and R gradually decreases. I must admit that I was surprised to see Java ranked second. I thought r was second.

Fourth, Scala's growth is impressive. Few people used Scala three years ago, but now they can compete with other mature languages.

This is particularly evident when we switch from the trend diagram to the actual data view at indeed.com.

Fifth, Julia's popularity is relatively poor, but it must have increased in recent months. Will Julia become one of the popular languages for machine learning and data science? It's up to time to answer.

If we hide Scala and Julia to zoom in on the growth of other languages, we can be sure that Python and R are growing faster than other languages.

From this curve, perhaps r will soon surpass Java in popularity. When we change to "deep learning" to query, the data obtained is very different.

In this query, python still leads, but followed by C , Java and C. R is only in fifth place. It is clearly emphasized here that these are high-performance computing languages. As a general machine learning language, Java will soon occupy the second place, and R language will not reach the top in a short time. To my surprise, as the main language of the deep learning framework torch, Lua is not on the list, and Julia is also absent.

Should the answer to the original question be clear now? Python, Java and R are the most popular languages in machine learning and data science. If you focus on deep learning rather than general machine learning, you should learn more C . Secondly, C also needs attention and learning. Remember, however, that this is only one way to look at the problem. If you want to find a job in academia, or just want to study machine learning and data science in your spare time, you may get different answers.

As for my personal answer, I answered it on my blog earlier this year. In addition to supporting many mainstream machine learning frameworks, Python is very suitable for me, just because I have a background in computer science. I also like to use C to develop new algorithms, because I have been programming in C for most of my career. But this is just my personal situation. People with different professional backgrounds may feel that another language is better. Statisticians with limited programming skills will prefer R. Powerful Java developers can continue to use their favorite Java because Java has a considerable number of open source Java APIs. The same is true for other languages.

Therefore, my suggestion is to read other blogs that discuss the same problem, and then invest a lot of time in learning a language.

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