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Python vs R: Which programming language is better for data science? Posted on Oct 11 - 2017

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It’s a key question for many data scientists -- especially those that are new to the field: is Python or R better for data science?

For those first venturing into the world of data science, it’s important to master one language first, rather than looking to be a Jack of all trades from the offset. This is because your processes and techniques are what really matter most, and mastering these in one language before branching out into learning more is what is going to get you a strong footing in the data science world.

Once you have a strong set of skills and techniques under your belt, moving into other languages is a great way of skilling up and ensuring that you stay competitive in your field, but your first programming language should allow you to learn as much as you can. And there’s no shortage of languages that you can pick as your weapon of choice for doing so -- when it comes to data science, there’s plenty on offer, including (but not limited to): Java, C, C++, Scala, Perl, Clojure, Julia, and more. However, Python and R are undoubtedly the forerunners for the majority of the data science world.

So which do you pick?

For years, R has been the obvious choice for those going into data science -- it was designed with statisticians in mind, has a long history of success in the industry, has thousands of publicly released packages, and integrates well with programming languages such as C, C++, Java, etc. Released in 1997, R is common in a whole range of sectors -- it’s used by leading commercial companies such as Google and Facebook, and can be found from Wall Street to Silicon Valley as a good alternative to software such as Matlab and SAS.

On the other hand, Python offers plenty of benefits which mean that an increasing number of people are adopting Python for their work. As one of the most popular mainstream programming languages on the market, it’s a practical choice for tech types of all kinds -- data scientists included.

In particular, Python is taking off in the financial sector -- it’s now the Bank of America’s tool of choice for crunching financial data. It’s certain that Python is challenging R’s long-established position as the lingua franca for data scientists, but why? Here’s five reasons why you might choose Python for data science. View More

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