The key factor in choosing a programming language for a big data project is the goal at hand, and Python is currently the most popular language in the data science exploration and development stage.
Also popular in this area is R, while the SAS environment is still popular among business analysts and MATLAB also is frequently used for the exploration and discovery phase.
Another determinant of a data science language can be what notebook a researcher is using: Jupyter is the heir to the iPython notebook, and has close alignment with Python while also supporting R, Scala, and Julia.
Coders often will choose a different set of languages in terms of developing production analytics and Internet of Things applications, as they frequently rewrite the app and re-deploy the machine-learning algorithms using different languages than those used during experimentation. When speed and latency are imperatives, many programmers opt for C and C++.
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