A powerful language in the world of Data Science and statistical computing, R is getting increasingly popular among students. After it was developed at the beginning of the 1990s, there have been endless efforts made to improve the user interface of the programming language.

A powerful language in the world of Data Science and statistical computing, R is getting increasingly popular among students. After it was developed at the beginning of the 1990s, there have been endless efforts made to improve the user interface of the programming language.

In its journey from being a rudimentary text editor to becoming an interactive R Studio and then going on to be Jupyter Notebooks, R has kept the world Data Science communities engaged.

But learning R could be frustrating if it is not approached the right way. You are probably familiar with student reviews documenting the struggle with the language. There would be some who gave up midway, and there are still some who feel stuck and are desperately looking for a more structured way to approach it.

Whether you fall in these categories or are a fresher, you may be relieved to know that the language does have some inherent issues. So quit being harsh on yourself if you find it difficult. Usually, there is a clear mismatch between the source of your motivation and what you are learning.

Nobody wants to engage with dry practice problems and coding syntax because they love these rather boring activities. Absolutely not! People want to bear with this long, arduous process of mastering the syntax because it will allow them to graduate to the good stuff. However, the mountain of complicated and lengthy topics that you have to cover to be able to do something with it can be painful.

*And if you have arrived here to find out if there is a more natural way to reach your goal, you are where you should be.*

There is a more structured way to learn R and believe me it is worth learning! For anyone interested, there are some definite advantages of learning R over the other programming languages. Most importantly, the everyday tasks in Data Science can be conducted straightforwardly with the tidyverse ecosystem of R. The Data Visualisation in R programming language is both simple and powerful. It also has one of the friendliest and most inclusive online community which you will find very helpful.

If you want to learn R, you need to be very clear about what you are dealing with and get a comprehensive view of the big picture. That is exactly what we will be doing here. For starters you are expected to have a lot of doubts regarding R, starting from the basics of what it means and why learn R? it to the more complex areas of data analysis, data manipulation and machine learning. Let us tackle the aspects one by one as we guide you towards the right way of learning R.

The R Foundation has described r as “a language and environment for statistical computing and graphics.” That is to put it very simply because R is clearly a lot more than that.

Below is a list of characteristics that have become definitive of R as a programming language:

- A data analysis software: For anyone wanting to make sense of data, R can be used for Data Visualisation, statistical analysis, and predictive modeling.
- A programming language: R is an object-oriented language that provides operators, functions, and objects to make it possible to explore, visualize, and model data.
- An open-source software project: Although free, the numerical accuracy and standard of quality in R is very high. The open interfaces of the language allow its easy integration with other systems and applications.
- A statistical analysis environment: R is where some of the most cutting-edge research happens in predictive modeling and statistics. This is why R is often the first platform to offer a newly developed technique after it arrives. Even for the standard statistical methods, implementation in R is really easy.
- A community: With a large online community, R has about two million users! It should not be surprising that the R project leadership includes leading computer scientists and statisticians.

In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.

Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

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