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R vs Python, why each is better

A report on a free-wheeling Australian meetup discussing "Why R is Better" and "Why Python is Better". What do you think?

By Gregory Piatetsky, @kdnuggets.

Phil Brierley sent me a report on a Data Science Melbourne data science meetup group in Melbourne, where they recently had a very good session with two speakers giving overviews of R and Python, and arguing why each one is better. The videos are on Youtube and below, and the R one is particularly entertaining.

Related to that, here are the results of a KDnuggets Poll which asked:

If used either R or Python for data analysis/data mining, did you switch,

Results: R has a big lead, but Python is gaining R vs Python

R Corner
R is a free, open-source language and interpreter that is designed for reading, handling, and analyzing data, and for reporting the results. We'll introduce R and its motivating concepts, spend some time getting familiar with R's strengths, brush dismissively over its weaknesses, and make inflammatory comparisons with other languages. We'll talk about tasks for which R is particularly well suited, and why, and demonstrate its use in situ.

Andrew Robinson has been messing around with applied statistics in various guises since the 1980's. He started using S-plus (V3.3) in the late 90's and escaped to R in about 2002, to get away from the GUI disaster of S-plus 2000. His analytical work has covered forestry, ecology, oncology, risk analysis, and biosecurity. He is now Deputy Director for CEBRA, the Centre of Excellence for Biosecurity Risk Analysis, and Reader and Associate Professor in Applied Statistics at the University of Melbourne.

Python Corner
Python is super easy to learn. It's logical, well documented and well supported. It also has an amazing and approachable data stack which can be used for analytics, visualisation, machine learning and even building end-to-end production systems. This talk will give an intro to those things.

Chris Hausler is a Data Engineer at Zendesk. Previously he's held the titles of data scientist, student, consultant, programmer and before that student again. Throughout all these things he loved playing with data and he still does.