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For a growing number of people, data analysis is a central part of their job. Increased data availability, more powerful computing, and an emphasis on analytics-driven decision in business has made it a heyday for data science.
Here is a brief description about the two ecosystems:
R: It is the Open source, which has traditionally been used in academics and research. Because of its open source nature, latest techniques get released quickly. There is a lot of documentation available over the internet and it is a very cost-effective option.
Python: With origination as an open source scripting language, Python usage has grown over time. Today, it sports libraries (numpy, scipy and matplotlib) and functions for almost any statistical operation / model building you may want to do. Since introduction of pandas, it has become very strong in operations on structured data.
Availability / Cost
R and Python, are completely free.
Ease of Learning
R has the steepest learning curve among the 3 languages listed here. It requires you to learn and understand coding. R is a low level programming language and hence simple procedures can take longer codes.
Python is known for its simplicity in programming world. This remains true for data analysis as well. While there are no widespread GUI interfaces as of now, I am hoping Python notebooks will become more and more mainstream. They provide awesome features for documentation and sharing.
R has highly advanced graphical capabilities along with Python. There are numerous packages which provide you advanced graphical capabilities.
With the introduction of Plotly in both the languages now and with Python having Seaborn, making custom plots has never been easier.
Python has had great advancements in the field and has numerous packages like Tensorflow and Keras.
R has recently added support for those packages, along with some basic ones too. The kerasR and keras packages in R act as an interface to the original Python package, Keras.
We see the market slightly bending towards Python in today’s scenario. It will be pre-mature to place bets on what will prevail, given the dynamic nature of industry. Depending on your circumstances. Researchers and statisticians choose R as an alternative because it helps in heavy calculations. As they say, R was meant to get the job done and not to ease your computer. Python has been the obvious choice for startups today due to its lightweight nature and growing community. It is the best choice for deep learning as well.