Why Learn Data Science with R Programming?

By | May 24, 2020
Why Learn Data Science With R programming

In the world of Data Science, R is one of the most popular programming languages. In today’s world, when data is all you need, Data Science is at its peak. People are lenient in sharing their confidential data, even their unique identification number or bank account numbers. Data Science is all about extracting useful insights from the raw data available. In the field of Data Science such as Python, SQL, R, and many more. Over the years, R has been gaining much popularity as it was conceived to be a language of statistical computing. R Consortium says, ‘A broad range of organizations have adopted the R language as a Data Science platform including finance, biotech, research and high-tech industries.’

This article lets you understand why you should learn R by doing self-study or preferably through a Data Science with R certification training.


What is R?

R is a programming language that provides an environment for statistical computing and graphics, developed in 1993 by Ross Ihaka and Robert Gentleman. This language has an extensive catalog of graphical and statistical methods and includes linear regression, machine learning algorithms, time series, statistical inference, and many more features.

R is used by academics as well as large companies like Airbnb, Facebook, Google, and more.

A series of steps are undertaken to perform data analysis using R. The steps are as follows:

  • Programming
  • Transforming
  • Discovering
  • Modeling
  • Communication

R is a clear and accessible programming tool that comprises a collection of libraries written in R. Though some of them are written in C, C++, and FORTRAN for heavy computational tasks. These libraries are designed specifically to be used in Data Science. When you need to discover the data, you perform data investigation, refine hypothesis, and then analyze. For creating a model, R has a wide array of tools that enable you to create the right model for your data. For communicating the results, it is required to integrate codes, graphs, and outputs to a report using R Markdown, or you can build Shiny apps to share globally.

By now, you know that R is a programming tool that is widely used in Data Science. Let us now look at the reasons why you should use R programming.

Why Should You Learn R?

The major reasons that answer the above questions are:

R is Important for Data Science

R does not need a compiler to run; it is an interpreted language. So the code can run without any compiler. R can interpret the code, and thus the development of code is made easier.

You can add functions to a single Vector without the need to put it into a loop because R is a vector language. R is a statistical language that can be used in biology, genetics, and statistics.

R is Free and Open-Source Code

R is available for free, and it is open-source, which implies that anyone can download and edit the code. R is stable and reliable because being free and open-source, many excellent programmers have done modifications and improved the code to fix the R code.

There are no restrictions on the usage of R as it is issued under the General Public Licence. The only thing you need to keep in mind is that if you modify or redistribute the R source code, it is required that the changes are made available for others to use.

R Runs Anywhere

R is available for different types of hardware and software as the R Development Core Team has tried its best to make it run anywhere. Hence, R can now run on Windows, Unix systems, and the Mac.

R Supports Extensions

R can perform a wide range of functions like data manipulation, statistical modeling, and graphics. One important feature that makes usage of R beneficial is its extensibility. Developers are allowed to develop their software and distribute it by forming add-on packages. Thousands of packages exist on this platform just because it is very easy to create these packages. Many new statistical methods are published with R. This feature makes R a developer-friendly language that allows modifications and updates in its tools.

R Provides Extensive Community Support

R has an active community where developers start helping new users and guiding the use of R in their professional areas. This language provides many worldwide workshops and boot camps worldwide. They can become active on Question & Answer websites like Stack Overflow or R mailing lists. R users can also take active part on social networking sites like Twitter and regional R conferences.

R Facilitates Interaction with Databases

Several add-on packages can connect R with databases like the RODBC package that can read from databases like Open Database Connectivity Protocol(ODBC). Also, the ROracle package is there that allows interactivity with Oracle databases. The extensions to MySQL are also provided by R, which is in the form of RMySQL.

R can also be connected to other languages like C, C++, Python, Java, and many other popular languages.

R is simple and easy to learn

R is a statistical language, and therefore many people say that it is difficult for beginners to learn the R language. However, when you start learning, you find it simple and easy to learn. If you wish to use R at its best and leverage its features, you are required to have deep knowledge of statistics. The syntaxes in R are so easy to understand that they allow you to learn and use them proficiently.

There are various packages for data wrangling like dplyr, purr in R. it provides extensive support for data modeling. Aesthetic visualization tools make R best suited for Data Science. Using R, you can easily analyze structured as well as unstructured data. R can be used for Data Science applications like ETL or Extract, Transform, Load.

Over to You

By now, you have read about features of R so that it can be used in Data Science applications. Data Science is booming these days, hence learning R can be beneficial for you. There are online training courses available by accredited training institutes that make it very easy to learn R and implement in Data Science. So, why not put efforts into upskilling and taking your career ahead!

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