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It's hard to know whether to use Python or R for data analysis. And that’s especially true if you're a newbie data analyst looking for the right language to start with. But it is possible to figure out the strengths and weaknesses of both languages. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer: What should I use for machine learning? I need a fast solution, so should I use Python or R? Python vs. R for Data Analysis At DataCamp, we often get emails from learners asking whether they should use Python or R when performing their day-to-day data analysis tasks. Both Python and R are among the most popular languages for data analysis, and each has its supporters and opponents. While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R's functionality was developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization.

Antes de seleccionar el lenguaje a utilizar para el trabajo en ciencia de datos, también es importante considerar cuestiones como las tareas específicas que se realizarán y si estas van a correr en una computadora de escritorio o una portátil (laptop), si se va a utilizar servidores locales, remotos o en la nube. También se debe considerar, si el trabajo se lo hará para academia, donde R es muy popular o será para la industria, donde predomina Python.