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R Studio is an integrated software environment for the R programming language.
R is an agile and substantially powerful language with widespread applications in statistical data analysis, computation, data science, and AI. The R Core Team and the R Foundation developed R using C, Fortran, and R itself. As a result, the R programming language is one of the common languages used by data miners and by software engineers for developing data analysis and statistical applications.
It boasts of:
R is one of the most prominent data analysis applications today. It is developing rapidly and has a vast community of developers who support the language & the suite with third-party packages.
Given its intricate applications, many third-party packages, and not-so-beginner-friendly syntax, R can pose severe challenges for many. And while R Studio helps one run R in a friendlier environment via a graphical user interface, it too can be a tough nut to crack.
The challenges of R and R Studio stem primarily from this potency & flexibility. However, certain design decisions also contribute to its steep learning curve.
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Our R subject matter experts are thorough with the nuances of R & R Studio. They have worked on R Studio assignments involving elementary programming, computation, statistical analyses, data science, and machine learning.
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Mastery of R requires in-depth knowledge of its numerous features and a minute understanding of how to apply them with prudence.
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The above list offers a highly inexhaustive look at the R topics & concepts we cover.
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Ans.: Statistics can be tough for many. You need to be fluent in number theory, probability, analytical geometry, and set theory. In addition, it would be best if you had solid ideas about data interpretation & visualization and understood how mathematical concepts are used to dissect statistical data.
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