Nice infographics by DataCamp. Click here to view the original and commented version.
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Join Data Science Central
I simply love R as statistical tool and also as programming tool (a component which is quite scarcely explored in media). Although the mainstream to data science originates in computer science/engineering, I remain partial to data scientists that come from mathematics/statistics background; it takes years to master it as opposed to months to learn decent coding. From this perspective, I feel that schemes and taxonomies like the one presented above are simply interesting and informative if not ephemeral.
I am not fully convinced by the "pluses" for Python, while most of the "minuses" for R have long since vanished.
Interesting article, but the graphic on movement of supporters for R / Python / Other could have been more helpful if all figures were on the same base. With a lot of calculations, I found that R had moved to 73%, Python 87%, and Other to only 18% (presumably of those who filled in a survey) IF I've got it right, perhaps someone could confirm or not. We're supposed to open up the data for others to understand, not to have to do data science (almost) to work out what has actually happened. But then, I'm sure I've done just the same to others plenty of times.
Very thorough analysis
Through and Precise!
I like R, but my vote goes to python as it can be used to make 'intelligent' decisions on the fly and especially in scenarios when the ecosystem is user driven. Dynamic inputs and dynamic 'smart' outputs will change the definition of the web.
I wanted to like Python, but it does not handle categorical data well. With R, you can through continuous and categorical data at it with abandon and easily create a predictive model. With Python / sklearn, categorical data requires too many compromises and too much data munging. Ironically, I moved to Python because it's a real programming language that's great at data manipulation.
R's big limitation for me was that it was inefficient on a single computer. You have to run it on some serious hardware to do anything even moderately large.
I moved back to R with R Studio and the data.table package. I'm playing with running R in the cloud. Requires a little bit of setup, but now I can handle very large tasks without significant limitations.
So, I'd suggest R for anyone serious about data science.
R seems to be gaining in popularity, according to the Popularity Rankings graph.
Hmmm Never searched for a TLDR on an infographic before
Amazing comparison. Looks like a photo finish for a marathon !!!
This is amazing !! Thank U very much :D
Very nice !
IMO, the most important and impactive difference is not clearly explained: R is licensed under GNU GPL while Python is permissive license BSD-like.
It means that if you want to distribute software under copyright you can't bundle it with R since the GPL License applies (R is not a mere programming language but an execution environment) or all the produced code would become GPL.
This can be a problem, and it doesn't apply with Python.
Welcome toData Science Central
Sign Upor Sign In
Or sign in with:
@DataScienceCtrl | RSS Feeds
The Role of Statistics in Data Science
How Automated ML is Transforming the Predictive Analytics Landscape
Analytics Scorecard: Rate your company
Reusable Analytic Workflows Empower the Citizen Data Scientist
Train AI 2017 Invitation - May 17, San Francisco
The Best Ways to Visualize Time Data
Last chance for Strata London Early Price (ends this Friday)
Get a Guaranteed Data Science Job
Gartner 2017 Magic Quadrant for Data Science Platforms
[eBook] Breaking Data Science Open
Added by Tim Matteson
Added by Tim Matteson
Python for Data Science
NoSQL and NewSQL
Internet of Things
© 2017 Data Science Central
Report an Issue |
Terms of Service
Please check your browser settings or contact your system administrator.