Introduction to R

Data Analytics with R

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

Purpose: 
Learn how to leverage R to perform data analytics
Audience: 
Developers, data scientists, and individuals responsible for creating analytics software.
Skill Level: 
Learning Style: 

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.

Hands On help
Duration: 
3 Days
Productivity Objectives: 
  • 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

Contact us to learn more

Not all training courses are created equal. Let the customization process begin! We'll work with you to design a custom Introduction to R training course that meets your specific needs.

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