R you ready to learn the hottest statistical programming language and environment with the easiest course yet! Enroll Now and master R!

Anyone who works in data science understands the grueling task they sign themselves up for and how much of an ally R Programming language can be when dealing with tons of statistical data. R is becoming a popular language and environment for building cutting-edge scientific algorithms and intelligent analytics applications.

R is an open source, free language and environment for statistical computing and graphics. It was designed by Ross Ihaka and Robert Gentleman, and written primarily in C, Fortran, and R and is a GNU package.

R provides a range of statistical and graphical techniques including linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc. It also simplifies the process of creating mathematical symbols and formulae, when required.

R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist.  

The course is meant for absolute beginners, so you don’t have to know anything about R before starting. But after this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.   

What will you learn in this course?

•What is R, a language and environment and how to install it into your systematic

•Getting started with R, including relationship with maths, vectors, arrays and matrices

•How to handle data in R, creating lists, data frames, functions and dealing with strings and factors

•how to control flow in R, including looping programs

•Importing and exporting data into and out of R

•How to use and update Date and Time functions

•How to handle packages, install them, and maintain them

•How to generate graphs such as Pie Charts, Bar Graphs, Line Graphs, Box Plots, and Scatter Plots