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Data Scientists and Programming: Speaking the Language of Data
The Python and R computing languages have become the two preferred languages among computer scientists. R was developed by statisticians specifically for data analysis and comes with strong data visualization capabilities. Python is a general-purpose scripting language with extensive libraries, object-oriented capabilities, and easy-to-understand syntax. [DataCamp has a great Infographic comparing the two languages.]
Since R was developed by statisticians and has more than 5000 packages (from CRAN, BioConductor and Github) that can be downloaded to further extend the base capabilities, R’s coverage of statistical methods is fairly exhaustive. The syntax of R is compact and powerful, and the command line it’s possible to interactively plot and explore the data. Generally, people working in research, data science and statistics prefer to use R.
Python has only become a challenger to what the R language can do because of statistical libraries that can extend Python core capabilities, like numpy, scipy, pandas and scikit-learn. Python programs tend to be much more readable, although longer, than similar programs written in R. Generally, people who work in a more general engineering environment prefer to use Python.
While R and Python are the two most popular languages used by Data Scientists, they aren’t the only ones. Performance is often a reason why other languages might be brought in to handle some jobs. Java and C/C++, for example, are often used for tasks that require good performance or are intended to be used in a production environment. A new high-performance language for data analysis called Julia is also attracting attention.
