Python vs R – Who Is Really Ahead in Data Science, Machine Learning?

Suppose you have an astounding idea for a Machine Learning application. It will change the world of finance, mobile advertising, or some other world, but it's certainly going to revolutionize something. You are going to develop the smartest, most learned app that world has ever seen, but how will you code your brilliant idea. Which programming language should you use for your app, with the two immediate candidates likely being Python and R?

Both programming languages have their pros, cons and die-hard fan-base. This article is intends to explore various use cases and evaluate the two bitter rivals, with regards to Machine Learning.

Case 1: Ease of Development

Python lets you hit the ground running… if you have programming experience.

Though Python and R have manageable programming and are popular among businesses and academia, Python among both lends itself more effectively to coders who are familiar with other programming languages. Python has familiar syntax than R as well as closer to regular English text – making it easier to read and debug.

While R has made strong position among advanced business users – e.g. data analysts in fields, including retail, marketing or finance – who come from more of a statistics background, rather than programming or software development.

As you are building up a machine learning application, we surmise that you're closer to the latter group – and in that case, you may find Python's flexibility, readability and similarity more familiar to the programming you already know and love.

Winner: Python

Case 2: Robustness and Production Readiness

Python fits more naturally into a complex coding environment.

In the business world, applications of R are certainly in a development direction. On the other hand, Python is as yet a more full-fledged programming language and found ample usage in different types of web and other applications along with its application in data science. R, then again, is still utilized for data analysis advanced statistical modeling. 

Subsequently, imagine you would want to integrate your machine learning algorithms into some kind of interface that communicates with other code, written by other coders, Python might be a better choice there. R has the best usage in rapid prototyping or to solve a particular issue, but Python will be easier to maintain and scale in the long run (particularly considering its versioning and documentation are far more consistent).

Winner: Python

Case 3: External Libraries

While R and Python, both have a breadth of external libraries, thus both are eligible to be used in a machine learning project. But Python has a bit more mature library. It packed with numpy, pandas, matplotlib, scikit-learn that is extremely popular, open-source machine learning package that is used in numerous business applications. 

However, R libraries such as caret are getting high popularity but don't place themselves there yet where high broadness of functionality is required. With R, you may get the ability to more rapidly create and launch your first model. But developing skills in scikit and similar libraries will serve you with a more profound and more complete toolset that will keep you safe when you use them in your machine learning app.  

Winner: Python

Case 4: Performance with Big Data

R can provide better performance when performing large computations.

Machine learning holds the capability to include working with large volume of datasets and exceedingly complex calculations in order to prepare and test algorithms. Hence, you will need to ensure the programming language you use will perform well in these kinds of scenarios.

While both R and Python can coordinate with Hadoop for Big Data, current R bundles use C to give better execution to vast scale calculations. Consequently, you may get faster outcomes when making use of R in these scenarios.

Winner: R

Case 5: Statistics and Data Visualization

Machine learning applications have a different mechanism than to machine learning software; hence it might incorporate some elements such as statistics, data analytics, and data visualization.

Here, R is available as a winner tool and it is designed from the beginning to make available a robust platform for advanced statistical analysis. Integrating ggplot2 there also empowering the development of something really agile visualizations as well such as interactive, browser-based graphs and charts.

Python is used widely for statistical analysis as well as for data visualization. R will likely be the better choice to enable such kind of functionality – particularly with regards to 'one-off' operations, prototyping and testing various hypotheses (as opposed to making reusable and extendable features).

Winner: R

So overall the champ is ….

Python is developed with all essential provisos that every application, use case and a business scenario is different. That makes Python more mature programming language that also facilitates fully-fledged and flexible option to develop machine learning apps as well as to create more complex coding projects in general. However, it wouldn't be a surprise if R surpasses Python within a few years since it's gaining popularity and significant development consistently.

What's your opinion on the Python versus R debate? Would love to hear from Data Scientists and Business Owners who are already using one of the two...