A Complete Guide to R for Data Science

Data Science is a multidisciplinary branch that is a combination of software engineering, scientific methods, data engineering, business intelligence, visualization, and statistics. R is an open-source arithmetical programming language that is widely used in the Big Data industry to analyze the data. It was developed in the early 90s, and since then, it has only improved.

R language’s journey from being a simple text editor to immersive R studio and recently Jupyter Notebooks has involved many communities in the field of data science around the world. The inclusion of packages such as dplyr, readr, data.table, SparkR, and similar have made the language R more powerful. R is also a big part of the modern data science syllabus that helps ease taking the business-related decision.

An Overview of the Language R

R is an open-source programming and statistical language used to analyze, manipulate data, and visualize information.

  • Popular

R language has gained popularity over the years for programming. It is being accepted widely in the fields of data science and machine learning. It is also a big part of the data science syllabus that professionals study to gain in-depth knowledge of the industry.

  • For All Platforms

The language is available for every popular platform like Windows, Linux, and Mac. The code written in R language can be easily transferred to other platforms such as from windows to mac or Linux and vice-versa. Cross-platform interoperability is one of the significant features of the R language, which is especially beneficial for data science and machine learning.

  • Industry-oriented Skill

One of the important industry-oriented skills is having in-depth knowledge of R language if you want to excel in your data science syllabus. It also plays a significant role in landing a desired job role in the related industry. Data Scientist is also one of the highest paying jobs in the world which are in ever-increasing demand.

Today, all the companies want to add more value to their business and extracting useful data can become an essential asset for growth. R language plays a crucial role in data extraction as it is one of the statistical languages that is used in the process. With R, the data can be shaped into a format that can easily be accessed and analysed by slicing large multivariate datasets. You can smoothly perform such tasks if you have undergone enough training or gained experience from an elaborated and specialized data science course.

Why study R language in your data science course?

  • R is a leading programming tool for machine learning, data analysis, and statistics as functions, objects, and packages that can easily be created. It is also available for free use and is platform independent. As it can be installed in any operating system, you can use it for commercial and individual purposes.
  • R programming language is not just any statistic package but also allows you to integrate with other languages like C and C++. Since you can easily interact with many statistical packages and data sources, it is widely gaining popularity in large programming communities. Therefore, it can help you land better job roles anywhere across the world.

Job Roles After Completion of Data Science Syllabus

  • Data Scientist (IT)
  • R Data Scientist
  • Analyst Manager
  • Business Analyst
  • Analyst Consultant

Learning Data Science with R

Including R programming language in your data science syllabus will boost your chances of landing a better job than the usual. In India, you can learn such technical skills from a data science course from reputed institutes like Jigsaw Academy. The specialized course of Data Science will help you gain knowledge of the advanced concepts of data science and machine learning, along with programming skills.

Stay curious and keep learning the advancements of the field to reach new heights in your career with the help of a data science online course.

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