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.