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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/
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