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R Programming for Data Science

Learn R Programming Fundamentals, Data Wrangling, Data Visualization for Data Science

     
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₹2499

This Course Includes

  • iconudemy
  • icon5 (78 reviews )
  • icon8 total hours
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconUdemy

About R Programming for Data Science

Welcome to this course of R Programming for Beginners with the hands-on tutorial, and become an R Professional which is one of the most favoured skills, that employer's need.

Whether you are new to programming or have never programmed before in R Language, this course is for you! This course covers the R Programming from scratch. This course is self-paced. There is no need to rush - you learn on your own schedule. 

R programming language iѕ one of the best open-source programming language and more powerful than other programming languages. It iѕ well documented and has a clean syntax and quite еаѕу tо lеаrn.

This course will help anyone who wants to start a саrееr in Data Science and Machine Lеаrning. You need to have basic undеrѕtаnding оf R Programming to become a Data Scientist or Data Analyst.

This course begins with the introduction to R course that will help you write R code in no time. Then we help you with the installation of R and RStudio on your computer and setting up the programming environment. This course will provide you with everything you need to know about the basics of R Programming.

In this course we will cover the following topics:

Basics of R Programming including Operators

Fundamentals of R Programming

Vectors, Matrices, Lists

Data Frames

Importing Data in Data Frames using Text and CSV files

Data Wrangling using dplyr package

Data Visualization

This course teaches R Programming in a practical manner with hands-on experience with coding screen-cast. 

Once you complete this course, you will be able to create or develop R Programs to solve any complex problems with ease.

What You Will Learn?

  • Fundamentals of R Programming.
  • Work with RStudio.
  • Use Vectors, Matrices, Lists, Data Frames.
  • Importing and Handling Large CSV files Data in R.
  • Import packages in R & use dplyr Package for Data Wrangling.
  • Create Data Visualization in R.
  • Using R for Basic Statistical Data Analysis.