R is a popular open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students and covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data are also included.

Audience: This course is designed for developers and data analysts.
Course Duration: 3 days
Prerequisites:

A basic programming background is preferred.

Hardware and Software Requirements:

Participants will need a modern laptop with the latest R studio and R environment installed.

Course Outline:
  • Language Basics (one day)
    • Course Introduction
    • About Data Science
      • Data Science Definition
      • Process of Doing Data Science
    • Introducing R Language
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • R Scalars, Vectors and Matrices
      • Defining R Vectors
      • Matrices
    • String and Text Manipulation
      • Character Data Type
      • File IO
    • Lists
    • Functions
      • Introducing Functions
      • Closures
      • lapply/sapply Functions
    • DataFrames
    • Labs
  • Intermediate R Programming (one day)
    • 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
    • Labs
  • Advanced Programming with R (one day)
    • Statistical Modeling with R
      • Statistical Functions
      • Dealing with NA
      • Distributions (Binomial, Poisson, Normal)
    • Regression
      • Introducing Linear Regressions
    • Recommendations
    • Text Processing (tm package / wordcloud)
    • Clustering
      • Introduction to Clustering
      • KMeans
    • Classification
      • Introduction to Classification
      • Naive Bayes
      • Decision Trees
      • Training using Caret Package
      • Evaluating Algorithms
    • R and Big Data
      • Hadoop
      • Big Data Ecosystem
      • RHadoop
    • Labs