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A Guide to Python and R: When to Use Which for What

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pythonby Angela Guess

Roger Huang recently wrote in The Next Web, “At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. This is an excerpt of our free, comprehensive guide to getting a job in data science that deals with two of the most common tools in data science, Python and R.”

Huang begins with Python: “Python is a versatile programming language that can do everything from data mining to plotting graphs. Its design philosophy is based on the importance of readability and simplicity… As you can imagine, algorithms in Python are designed to be easy to read and write. Blocks of Python code are separated by indentations. Within each block, you’ll discover a syntax that wouldn’t be out of place in a technical handbook. Many data scientists use Python to solve their problems: 40 percent of respondents to a definitive data science survey conducted by O’Reilly used Python, which was more than the 36 percent who used Excel. This has spawned one of the largest programming communities in the world, filled with brilliant and versatile problem-solvers who collaborate together to push Python forward. You’ll be sure to find any answers to your Python questions on StackOverflow or on Quora.”

Read more here.

Photo credit: Flickr/ Chris Parker2012

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