https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis

https://www.researchgate.net/post/What_is_are_the_best_wireless_EEG_headset_systems_for_research_high_temporal_and_spatial_resolution_How_much_does_it_cost

Choosing R or Python for data analysis? An infographic

May 12th, 2015

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 R Programming Python Learning Data Science

The DataCamp Team

I think you'll agree with me if I say: It's HARD to know whether to use Python or R for data analysis. And this is especially true if you're a newbie data analyst looking for the right language to start with. It turns out that there are many good resources that can help you to figure out the strengths and weaknesses of both languages. They often go into great detail, and provide a tailored answer to questions such as "What should I use for Machine Learning?", or "I need a fast solution, should I go for Python or R?". In today's post, I present to you our new Infographic "Data Science Wars: R vs Python", that highlights in great detail the differences betweens these two languages from a data science point of view. So that next time you are debating R vs Python for machine learning, statistics, or maybe even the Internet of Things, you can have a look at the infographic and find the answer.

R vs Python

At DataCamp, we often get emails asking whether one should use R or Python when performing their day-to-day data analysis tasks. Both Python and R are amongst the most popular languages for data analysis, and have their supporters and opponents. While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R's functionality is developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization. Our new infographic"Data Science Wars: R vs Python" is therefore for everyone interested in how these two (statistical) programming languages relate to each other. The infographic explores what the strengths of R are over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.Interested in some of our other infographics? Check out Statistical Language Wars and How to Become a Data Scientist in 8 Easy Steps