R vs Python in Datascience

Data science deals with identifying, representing and extracting meaningful information from data sources to be used to perform some business logics.The data scientist uses machine learning, statistics, probability, linear and logistic regression and more in order to make out some meaningful data. Finding patterns and similar combinations and cracking the best possible path way according to the business logic is the biggest job of analysis.

R, Python, SQL, SAS, Tableau, MATLAB, etc. are of the most useful tools for data science, R and Python being the most used ones. But still, it becomes confusing for any newbie to choose the better or the most suitable one among the two, R and Python. Let’s try to visualize the difference.

Overview :

R Python
R is a programming language and free software environment for statistical computing and graphics, supported by the R Foundation for Statistical Computing. It was designed by Ross Ihaka and Robert Gentleman and first released in August, 1993. It is widely used among statisticians and data miners for developing statistical software and data analysis. Python is an Interpreted high-level programming language for general purpose programming. It was created by Guido Van Rossum and was first released in 1991. Python has a very clean and simple code syntax. It emphasizes code readability and thus debugging is also far more simpler and easier in Python.

Specialities for datascience :

R Python
R packages cover advanced techniques which very useful for statistical work. The CRAN text view provides you with many useful R packages. R packages cover everything from Psychometrics to Genetics to Finance. On the other hand, Python, with the help of libraries like SciPy and packages like statsmodels, covers only the most common techniques. R and Python are equally good for finding outliers in a data set, but for developing a web service to enable other people to upload datasets and find outliers, Python is better. People have built modules to create websites, interact with a variety of databases, and manage users in Python. In general, to create a tool or service that uses data analysis, Python is a better choice.

Functionalities :

R Python
R has inbuilt functionalities for data analysis. R was built by eminent statisticians with statistics and data analysis in mind, so many tools that have been externally added to Python through packages are built in R by default. Python is a general purpose programming language. So most of the data analysis functionalities are not inbuilt and are available through packages like Numpy and Pandas, which are available in PyPi(Python Package Index).

Key domains of application :

R Python
Data visualization is a key aspect of analysis, as visual data is best understood. R packages like ggplot2, ggvis, lattice, etc. make data visualization easier in R. Python is catching up with packages like Bokeh, Matplotlib, etc. but is still far behind in this regard. Python is better for deep learning. Packages like Lasagne, Caffe, Keras, Mxnet, OpenNN, Tensor flow, etc. allows development of deep neural networks far more simple in Python. Although some of these, like tensor flow, are being ported to R(packages like deepnet, H2O, etc.) but it is still better in Python.

Availability of Packages :

R Python
R has hundreds of packages and ways to accomplish needful data science tasks. Although it allows to have desired perfection in completing the task, it makes it difficult for inexperienced developers to achieve certain goals. Python relies on a few main packages, viz., Scikit learn and Pandas are the packages for machine learning data analysis respectively. It makes easier to accomplish required tasks but consequently it becomes difficult to achieve specialization.

Ultimately it’s the job of data scientist itself to choose the most suitable language as needed. For statistical background, R might be a better option. But for the CS background or even a beginner, Python is most suitable option. But, it’s better to have sound knowledge of both cause both might be useful at times in data science career.



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