Session 1 of 4 | A video Introduction to R and Microsoft R Server
In session 1 of this four-part series, Matt Parker will introduce you to the fundamentals of Microsoft R Server, a big-data extension to the open-source statistical programming language R. In this session, Matt discusses how MRS enables R to process huge data sets by moving data from memory to disk and enabling parallel processing for statistical and machine learning algorithms
#About the Course
#Prerequisites
There are a few things you will need in order to properly follow the course materials:
* A laptop
* Basic programming experience, in any language
* A basic understanding of statistics and the data analysis process
#Modules
The course is divided into the following modules:
1. Introduction to R and Microsoft R Server
#Concepts Covered
1. Strengths and weaknesses of open-source R
2. How Microsoft R extends open-source R for big data
3. How to read data into open-source R
4. How to read data into the Microsoft R XDF file format
5. How to clean data with open-source R functions
6. How to clean data with MRS functions
7. How to build predictive models with open-source R
8. How to build predictive models with MRS
#Technologies Covered
1. Open-source R
2. Microsoft R Server
#Skills Taught
At the end of the course you will havev acquired the following skills:
1. Identify the strengths and weaknesses of open-source R.
2. Explain how Microsoft R Server extends R's capabilities.
3. Load data into R (in-memory) and into a Microsoft R XDF file.
4. Clean data with R and MRS functions.
5. Build a simple predictive model using R's lm() function and MRS' rxLinMod(), and understand the differences in the returned model objects.
