Machine Learning Times
Machine Learning Times
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This excerpt is from InfoWorld. To view the whole article click here

9 years ago
In data science, the R language is swallowing Python


According to a new survey, Python’s data science training wheels increasingly lead to the R language.

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According to a new survey of data professionals, Python remains the No. 1 tool for data science. As the report authors conclude, “Python is definitely top dog when it comes to data.”

There’s reason to believe Python’s dominance won’t last.

Two years ago I argued that Python would rule data science due to its breadth of utility and ease of use. But something interesting has happened. Data science, once the province of PhD propellerheads, has gone mainstream.

As data science has become an essential ingredient for so many businesses, so too has the R language.

Squeezing Python

It’s always precarious to compare programming languages, given their very different use cases. It’s easy to compare the relative popularity of Swift and C++, for example, but not necessarily very informative.

The same holds true of Python versus R, despite both being used by data science professionals. While R is a language developed by and for statisticians, Python has a more general-purpose existence. As such, there are far more jobs available to Python programmers, given its utility for developing Web applications and beyond.

What’s interesting and instructive, however, is to show relative growth in popularity.

According to IEEE Spectrum’s multifaceted ranking, the top five programming languages — Java, C, C++, Python, and C# — have stubbornly refused to budge over the past year. Not so R, which has been on a tear. Measured across Google searches, jobs, and more, R leaped from ninth place to sixth place over the course of a year.

On StackOverflow, the number of questions about Python has risen to triple that of the R language.

That’s huge. But as Revolution Analytics’ Andrie de Vries notes, the number of Stack Overflow questions about Python has grown to triple that of R questions.

Move beyond general interest (and general-use cases) and dig into jobs data, however, and highly qualified interest in R has boomed over the past year, even as Python has slipped a place (left column is 2015, right column is 2014).

While Python’s job slip must have multiple causes, R owes its rise to data science — lots and lots of data science.

In fact, a quick glance at job trends shows “data science” dramatically outpacing both R and Python. You can bet those data scientists are using one or the other language … or, more likely, both.

Snacking on Python, feasting on R

Within the burgeoning big data realm, we’re likely to see a melding of the two tools: Python, the developer-friendly generalist data language, and R, the data expert’s language. The question in my mind is whether we’ll need both long term.

But there’s reason to think adoption of R for data science will surpass that of Python. As data science becomes more and more foundational to business, it’s possible that R could actually leap ahead of Python in general popularity, not merely in data science.

As data science has taken off, some developers are using their Python skills to don a data science hat. Martijn Theuwissen of DataCamp puts it this way: “Python is used by programmers that want to delve into data analysis or apply statistical techniques, and by developers that turn to data science.” In short, says Theuwissen, “The closer you are to working in an engineering environment, the more you might prefer Python.”

Think of Python as the on-ramp to data science, but not necessarily the autobahn.

Matt Asay, VP of Mobile, Adobe
This excerpt is from InfoWorld. To view the whole article click here.

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