R is a popular programming language used for statistical computing and graphics by developers. This open sourced tool is not only just a programming language but also an excellent IDE. One important field of its applications is data analysis. Statisticians and data miners largely prefer R to develop their statistical software. However, R is not as popular as programming languages such as Java or Python. This article discusses the importance of R in the current era where data is everything.
How important is R?
We know that programming software like python offers an easy to understand syntax and higher versatility. Yet, R is preferred among data analysts. The reason for is that R was designed for statisticians. Hence R comes with field-specific advantages such as great data visualization features. A large number of major organizations are found using R in their operations. Google not only uses R but developed the standards for the language which got wide acceptance.
Revolution Analytics, kind of a commercial version of R was purchased by Microsoft and they provided servers and services on top of it. So, in general, despite the steep learning curve and uneasy syntax, R has its own advantages and the industry has recognized it very well.
In the opinion of experts, R is expected to remain as an indispensable resource for the data scientists for a very long time. The wide range of pre-defined packages and libraries for the statistical analyses will keep R in the top. The introduction of platforms such as Shiny has already resulted in increased popularity of R, even among the non-specialists.
So, Should You Continue Taking that Course Teaches Machine Learning via R?
It is known that every professional with a machine learning certification has huge career opportunities waiting ahead. But it is important to possess the exact skills the employers are looking for. So, is R such a skill wanted by employers? Well, it is observed that organizations are moving towards Python at a slow pace. In academic settings and data analysis R is still most popular, but when it comes to professional use, Python is leading. Python has achieved this by providing substantial packages similar to R. Even though most machine learning tasks are doable by both languages, Python performs better when it comes to repetitive tasks and data manipulation. A better possibility of integration is another advantage of Python. Also, your project may consist of more than just statistics.
It is recommended to start learning Python if you haven't spent much time with your Machine Learning course that teaches through R. After learning python, you can use RPy2 to access the functionalities offered by R. In effect, you will have the power of two different languages in one. Since most of the companies have production systems ready for this language, Python is always production-ready. Even if you feel like learning R after learning RPy2, it is pretty easy to do. But moving to Python after R is relatively much difficult. If you are already too deep in R, ignore everything and focus on it.