R is the most popular environment and language for statistical analyses, data mining, and machine learning. The number of libraries with new analytical functions is enormous and continuously growing. However, there are also some drawbacks. R is a programming language, so you have to learn it to use it. Open source development also means less control over code. Finally, the free R engine is not scalable. Microsoft started supporting R with SQL Server 2016 inside the database engine and as a standalone server, and continues to support it in later versions. A parallelized highly scalable execution engine is used to execute the R scripts. In addition, not every library is allowed in these two environments. You will learn the R language through a collection of courses under the common name EmbRaceR. In the first course, attendees learn to program with R from the scratch. Basic R code is introduced using the free R engine and RStudio IDE. A lifecycle of a data science project is explained in details. You will also learn how to use RStudio IDE, the most popular development environment for R.
EmbRaceR: Introducing R
R is the most popular environment and language for statistical analyses, data mining, and machine learning. The number of libraries with new analytical functions is enormous and continuously growing. However, there are also some drawbacks. R is a programming language, so you have to learn it to use it. Open source development also means less control over code. Finally, the free R engine is not scalable.
Microsoft started supporting R with SQL Server 2016 inside the database engine and as a standalone server, and continues to support it in later versions. A parallelized highly scalable execution engine is used to execute the R scripts. In addition, not every library is allowed in these two environments.
You will learn the R language through a collection of courses under the common name EmbRaceR. In the first course, attendees learn to program with R from the scratch. Basic R code is introduced using the free R engine and RStudio IDE. A lifecycle of a data science project is explained in details. You will also learn how to use RStudio IDE, the most popular development environment for R.
Modules
Module 01: Starting with R
When you talk about statistics, data mining and machine learning, many people, especially the ones working in academic areas, think about R. R is the engine and the language that the engine executes. You can use multiple different R environments - engines and development tools; however, the basic R language is only one. Of course, in order to use R, you need to learn how to program in this language. Module one is introducing the R language.
Lessons
Module 02: Working with data
In the second module, you will learn how to work with data in R. You will go extensively through string operations, and then deal with matrices, arrays, factors, and lists. You will learn about the most important data object, the data frame, and finally about the data frame enhancement, the data table.
Lessons
Module 03: Programming elements and distributions
The third module of this course introduces programming elements that help you create loops and branch the code execution. Custom functions are useful to encapsulate a part of the code that is frequently repeated and then modify it when needed in a single place. It is always useful to understand the most important distributions in statistics. R helps you to create demo data and graphs of many different distributions.