Since its development, R has always been the preferred programming language for Data Scientists and statisticians. First appeared in 1993, R is free software for statistical computing. Its popularity has increased manifolds recently due to innovations in Data Analytics field.
In this competitive business environment you don’t want to stay behind your competitor; therefore, one would not wish to waste any time on the wrong tool. To always stay one step ahead of your competitors, you should know which the appropriate tool for the task is and below are a few points that prove R is the best programming language for data science.
1. R is Data Science for Non-Computer Scientists
If you research about high-end data science tools, you will majorly get two choices, i.e. Python and R. Python is mainly the programming language for software engineers with sound knowledge of statistics, mathematics, and Machine Learning but this tool lags behind in library support for important subjects like Econometrics and many communication tools like reporting.
As the people who are interested in Data science are mostly from a business background instead being from Technicalities of programming and developing, learning Python is a challenge for them which has no such support for Econometrics. R, on the other hand, is a statistical programming language which has support libraries for Stats, ML, and data science. It is the best tool for data science for business because it provides itself entirely to its comprehensive support for topic-specific packages and the structure of its communication.Furthermore, R has support packages or libraries for Econometrics, Finance, etc. which is vastly used for business analytics. R is interactive to use and is easy as compared to intricacies of Python.
2. R is the Best All-rounder
Labeling R as just powerful is essentially an underestimation of the power it holds. R is not only powerful but smart as well as it has a robust infrastructure. It incorporates various algorithms comprising high-end Machine Learning package (H2O), XGBoost the top Kaggle algorithm, TensorFlow deep learning packages, and many more.
Tidyverse is the primary forte of R as this allows the ecosystem of application to be built using a more intelligent structural approach which is constant. It comes with libraries like ”tidyr”, “dpylr”, ”stringr”, “forcats”, ”lubridate”, and many more which streamlines the developing procedure even further. Therefore, it would be right to say R is an all-rounder.
3. Community Support
For any Programming interface or language to outdo its community support requires to be excellent, even if the product is the best but without community support, it is likely to be not used as there won’t be any assisting hand neither would there be the referrers. Like any other top programming languages, R has enormous community support.
All this make R stand out when it comes to business analytics by data science. Data Science with R program from a premier institute can help you learn this powerful data science tool effectively. Top institutions have quality resources and faculty to provide students hands-on experience on R.