Both R & Python should be measured based on their effectiveness in advanced analytics & data science. Initially, as a new comer in data science field we spend good amount of time to understand the pros and cons of these two. I too carried out this study solely for “self” to decide which tool should i pick to get in depth of data science. Eventually, i have started realizing that both (R & Python) has its space of mastery along with their broad support to data science. Here some understanding on “when-to-use-what”
Now, when you start getting into space of predictive modeling, machine learning and mathematical modeling, Python can give a easy hand. Mathematical functions, algorithmic problems find good support from Python libraries for k-means & hierarchical clustering, multivariate regression, SVM etc. Not limited to this, but it also has good support from data processing & data munging libraries like Pandas and NumPy. Here are some cents for python:
So in summary, we can bet on R when we start getting into statistical analysis and then eventually turn up towards Python to take your problem to a predictive end.
This write up doesn't meant to highlight R or Python's limitations. R has evolved as a good support to ML and does have combination with Hadoop as RADOOP. However, Python also has good support to statistics and does have rich library (matplotlib) for visualization. But, as i mentioned earlier in this write up, above finding points are solely based on ease of use while you learn Data Science. I suppose once matured we can develop expertise in any one on them as per job role.
Original post: http://datumengineering.wordpress.com/2014/02/08/r-python/
Comment
@Roland. Aim of this finding is to identify the right tool for swift learning and ad-hoc analysis. As far as Java is concerned my knowledge is limited to "LENSKIT" (http://lenskit.grouplens.org/) This is library is revealed by University of Minnesota professors in their learning program for Recommendation system. I haven't explored it much but i think it is limited to Recommendation system.
It is good comparison. How about JAVA?
I am completely unaware of RPy. Though, my post doesn't meant for filling gaps for limitations. But RPy would be interesting to explore. Thanks Louis.
Why not use Python with the RPy extension? It gives you really solid coverage of both functionalities.
© 2020 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central