In this course, you’ll be exposed to fundamental programming concepts in R. After the basics, you’ll learn how to organize, modify and clean data frames, a useful data structure in R. Then you’ll learn how to create data visualizations to showcase insights in data! Finish up with statistics and hypothesis testing to become a data analysis expert.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
The course will take you from Point A to B and at the end of this course you will have the confidence and experience to start writing codes and structure algorithms by yourself. We have also packed this course with multiple live real-world examples that will help you not only understand the theory behind R, but also how it actually works in application.
In this course we teach R programming language which is an interpreted language. An interpreted language does not need a compiler. That is, part of the program can be run without requiring all the program to be complete. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.