Data analysis is of course an important part of Big Data. Analyzing your data streams and combining different data sources for new insights can be done with a variety of tools and programming languages. Which programming language or tool is the right tool for the job at hand? That is an important question and especially for newbie data analysts it is difficult to determine whether to use Python or R for your data analysis project.
Fortunately, the guys from Datacamp have created a very detailed infographic on this hot topic. The below infographic combines multiple resources to gain a better understanding which programming language should be used for what project as well as explains more about the strengths and weaknesses of both languages. It shows in great detail the differences between the two programming languages from a data science perspective, so that as a data scientist, you can make the right choice.
Both Python and R can be seen as the most popular programming languages for data analysis and both have a large community that contribute to it. Both Python and R are easy to install and to get started with. Obviously, each language has is advantages and disadvantages for different projects and scenarios. As the developers of the below infographic state: Python is often praised for being a general-purpose language with an easy-to-understand syntax and Rs functionality is developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization
The below R vs Python infographic can therefore bring clarity for those who cannot decide upon R or Python as well as for those interested to gain a better understanding how bother programming languages relate to each other.
