Data Analytics With R Training Course

Day One: Language Basics

• 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
  • Matricies
• String and Text Manipulation
  • Character data type
  • File IO
• Lists
• Functions
  • Introducing Functions
  • Closures
  • lapply/sapply functions
• DataFrames
• Labs for all sections

Day Two: Intermediate R Programming

• 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 for all sections

Day Three: Advanced Programming With R

• Statistical Modeling With R
  • Statistical Functions
  • Dealing With NA
  • Distributions (Binomial, Poisson, Normal)
• Regression
  • Introducing Linear Regressions
• Recommendations
• Text Processing (tm package / Wordclouds)
• Clustering
  • Introduction to Clustering
  • KMeans
• Classification
  • Introduction to Classification
  • Naive Bayes
  • Decision Trees
  • Training using caret package
  • Evaluating Algorithms
• R and Big Data
  • Connecting R to databases
  • Big Data Ecosystem
• Labs for all sections