{"id":5903,"date":"2015-12-14T07:00:22","date_gmt":"2015-12-14T15:00:22","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=5903"},"modified":"2015-11-02T07:58:21","modified_gmt":"2015-11-02T15:58:21","slug":"data-scientists-and-programming-speaking-the-language-of-data","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/data-scientists-and-programming-speaking-the-language-of-data\/","title":{"rendered":"Data Scientists and Programming: Speaking the Language of Data"},"content":{"rendered":"

The Python and R computing languages have become the two preferred languages <\/a>among computer scientists. \u00a0R was developed by statisticians specifically for data analysis and comes with strong data visualization capabilities. \u00a0Python is a general-purpose scripting language with extensive libraries, object-oriented capabilities, and easy-to-understand syntax. \u00a0[DataCamp<\/a> has a great Infographic comparing the two languages.]<\/p>\n

Since R was developed by statisticians and has more than 5000 packages (from CRAN<\/a>, BioConductor <\/a>and Github) that can be downloaded to further extend the base capabilities, \u00a0R’s coverage of statistical methods is fairly exhaustive. \u00a0The syntax of R is compact and powerful, and the command line it’s possible to interactively plot and explore the data. \u00a0Generally, people working in research, data science and statistics prefer to use R.<\/p>\n

Python has only become a challenger to what the R language can do because of statistical libraries\u00a0that can extend Python core capabilities, like numpy, scipy, pandas and scikit-learn. \u00a0Python programs tend to be much more readable, although longer, than similar programs written in R. \u00a0Generally, people who work in a more general engineering environment prefer to use Python.<\/p>\n

While R and Python are the two most popular languages used by Data Scientists, they aren’t the only ones. \u00a0Performance is often a reason why other languages might be brought in to handle some jobs. \u00a0Java and C\/C++, for example, are often used for tasks that require good performance or are intended to be used in a production environment. \u00a0A new high-performance language for data analysis called Julia <\/a>is also attracting attention.<\/p>\n

 <\/p>\n

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The Python and R computing languages have become the two preferred languages among computer scientists. \u00a0R was developed by statisticians specifically for data analysis and comes with strong data visualization capabilities. \u00a0Python is a general-purpose scripting language with extensive libraries,…<\/span><\/p>\n

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