**The Introduction to R training course** introduces the fundamentals of the R programming language required to perform data analytics.

R is both a programming language and a software environment commonly use in creating statistical software, data mining, and data analytics.

During the Introduction to R course, you will learn the language fundamentals, commonly used libraries, and advanced concepts necessary to data analytics. In addition, we will explore common data analytics and graphing best practices.

## Course Summary

Hands-on training is customized, instructor-led training with an in-depth presentation of a technology and its concepts, featuring such topics as Java, OOAD, and Open Source.

- Understand how R and the R environment can be leveraged to perform data analytics
- Create a simple application using R
- Evaluate R against other analytics strategies like SAS, SPSS, Stata, etc.

## What You'll Learn

In the Introduction to R training course you’ll learn:

Day One: Language Basics

- What is Data Science
- Understanding the history of data science
- Definition of Data Science
- Commonly used tools and technologies
- Areas of application
- Understanding the Data Science Process

- Introduction to R Language
- What is R?
- R compared to other programming languages
- Setting up your R environment

- R Language Fundamentals
- Core R syntax concepts
- Variables and Types
- Control Structures (Loops / Conditionals)
- Writing your first R program

- Working with R
- R Scalars, Vectors, and Matrices
- Defining R Vectors
- String and Text Manipulation
- File IO

- Working with R Continued
- Lists
- Functions
- Introducing Functions
- Closures
- lapply/sapply functions

- DataFrames

Day Two: Intermediate R Programming

- Working with Data
- DataFrames and File I/O
- Reading data from files
- Data Preparation
- Built-in Datasets

- Visualization
- Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Map
- ggplot2 package ( qplot(), ggplot())
- Exploration With Dplyr

Day 3: Advanced Programming With R

- Statistical Modeling With R
- Statistical Functions
- Dealing With NA
- Distributions (Binomial, Poisson, Normal)

- Regression
- Understanding Linear Regressions
- Implementing Linear Regressions within R

- Recommendations
- Text Processing (tm package / Wordclouds)
- Clustering
- Introduction to Clustering
- KMeans

- Classification
- What is Classification?
- Naive Bayes
- Decision Trees
- Training using caret package
- Evaluating Algorithms

- Leveraging Big Data with R
- Understanding the Big Data Ecosystem
- Introduction to Hadoop
- Incorporating R with Hadoop using RHadoop

- Q/A