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This article mainly introduces "why Python is more popular than R in the field of data science". In daily operation, I believe that many people have doubts about why Python is more popular than R in the field of data science. I have consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the question of "why Python is more popular than R in the field of data science". Next, please follow the editor to study!

New data scientists are faced with a very important question: should I study Python or R?

This issue is very important because it takes a lot of time to learn the first programming language. It is impractical to try to have both, especially at the beginning of your career.

So which one should you choose?

In my experience, if you choose Python, your career will benefit more.

In my opinion, Python, especially in its infancy, is a better choice for data science.

I will explain why I chose Python in four points, but at the same time I would like to make it clear that this does not mean that R is a bad choice.

Choosing R will not have a negative impact on your work, and if your team has requirements, you must also learn from R. In fact, Facebook has used R as the analytical component of the internal investigation tool, which is supported by all of our data science infrastructure.

In other words, I think if you learn Python first, as a practical data scientist, you will become more efficient and better able to contribute to the team in important areas other than statistical modeling.

Therefore, after studying Python, you can bring more influence to the company, and your career will benefit more.

Reason 1: you always have to learn Python

Most companies not only require their data scientists to learn predictive modeling (that is, machine learning). At the very least, you may need to maintain the data pipelines that provide data to the model, which may be built with Python.

Today, the industry standard for piping is based on Python's Airflow, while at Facebook, we use basically the same internal Python tools.

In fact, I estimate that all Facebook data scientists use Python every week, while only about 10% of people use R regularly.

Therefore, choosing Python may be more effective: while some work can avoid using R, it is unlikely to avoid using Python.

The reason why 2:Python is easier to learn

The time you spend learning these skills is very important before you get a job, especially if you are self-taught outside of college.

Python is famous for its ease of learning. After learning both Python and R (though learning more about Python), I think Python deserves its reputation.

When you start to use language features other than statistical modeling, the advantage of Python's ease of learning is especially obvious. These features include packaging projects for distribution, developing command line interfaces, modeling data structures using ORMs, such as SQLAlchemy, and so on.

Using Python will make it easier for you to learn and master these features, and your career will benefit from it.

Reason why the 3:Python community is bigger

Source: Pexels

Python is one of the most popular programming languages in the world, and there is a large community on sites like stack overflow, kaggle, and even medium.

Therefore, when you encounter a problem that you can't solve, you will find it easier to find experienced people to ask for help and solve the problem.

This means that you don't have to spend too much time debugging compatibility with the system, so you have more time to deliver the code the company needs.

Reason why 4:Python is easier to deploy the model

Finally, you may reach a stage in your career where you want to provide the model to any end user in real time. To solve this problem, you need to build a web application based on REST, which is much easier to build using Python.

In fact, Python has some of the most popular web application frameworks in the world, namely Django and Flask. Your company's internal deployment tools are more likely to support these frameworks than R is.

The popularity of these frameworks also means that they are well supported by platform as a service providers (such as Heroku, Amazon Lightsail, etc.). You will be able to post personal projects online, which is a drop in the bucket compared to the cost of deploying the same project in R.

Most importantly, if you are lucky enough, your company uses the Python framework for its products, and learning Python means that connecting to intra-app tracking becomes very dangerous. If you can capture more functions for the model on your own, your personal influence will change dramatically.

Of course, all decisions have their own choices, as does choosing to study Python instead of R. Although I believe Python is a better choice for a career in data science, consider the disadvantages it brings.

For me, the biggest disadvantage of Python is that there are no tools equivalent to Rstudio. The most comparable tool in Python is Jupyter Notebook, but I personally think Rstudio is better because it has data exploration capabilities.

R is also popular in academia, so the documentation included in R is more likely to refer directly to academic research. These documents are very useful for data scientists engaged in "cutting-edge" research.

But I don't think the lack of tools equivalent to Rstudio can negate the comparative advantage of Python. There are also far fewer positions in data science academia, so R's research-related advantages are less important to most data scientists.

Therefore, although R has many advantages, I believe that if you choose to study Python, your career will benefit a lot.

Finally, it is worth mentioning that I do not think learning R is a bad choice. The most important thing is that no matter which language you choose, you should not stop there forever. There are always more similarities than differences between all programming languages: it is much easier to learn a second language than to learn the first one.

At this point, the study on why Python is more popular than R in the field of data science is over. I hope I can solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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