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Fundamentals of R Programming
Rating: 3.5 out of 5(4 ratings)
17 students

Fundamentals of R Programming

Fundamentals of R Programming : This is a complete course on R for beginners and covers the basics of R Programming
Created byRoshan Gangurde
Last updated 4/2021
English

What you'll learn

  • Data Analysis Skills
  • R Programming Language

Course content

6 sections12 lectures2h 17m total length
  • Introduction2:35

Requirements

  • Fundamental knowledge of any programming language
  • Motivated enough to learn a new programming language

Description

R is a widely used statistical programming language that’s beloved by users in academia and industry.

R works well with data, making it a great language for anyone interested in data analysis, data visualization, and data science.

It is a popular language used by data scientists and researchers. If you are working with data, R is a fantastic language to learn.

This is a complete course on R for beginners and covers the basics of R Programming language.

This course aims to provide a practical introduction to the R programming language.

This is a hands-on course.

Participants can write R code as a script.

This course introduces students to the R statistical programming environment.

The course is given in English.

Following are the course contents:

  • What is R Programming Language and Why to learn R Programming Language?

  • How to install R and RStudio?

  • Basics of R Programming

  • Data Structures in R: Vector, List, Matrix, Data Frame, Factor, Array

  • Control Structures in R: If, If-else, for loop, while loop, Statements: repeat, next, return, break

At the end of this course, the learner will be able to:

  • Install R and RStudio

  • Get hands-on with R Programming

  • Understand R Packages, their installation, and management

  • Understand R program Structure

  • Explore R Data Type, Command Syntax

  • Apply various Control Structures in R Script

  • Demonstrate various data structures available in R

Who this course is for:

  • Students who are pursuing professional graduate/post-graduate courses in Data and statistical analysis
  • Teachers/faculties who want to acquire new skill of data analysis
  • IT professionals from the data analysis industry