Data Analytics with R (IR)

Course Description Schedule Course Outline

About this Course

Data Analytics with R is a three-day, hands-on course that covers language fundamentals, libraries, advanced data analytics, and graphing with real world data. In this course you will learn how to program in R and how to use R for effective data analysis. You will also learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course also covers practical issues including programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

Who should attend

Developers who want to learn R programming language for data analytics

Class Prerequisites

  • Basic programming background is preferred
  • A modern laptop
  • Latest R studio and R environment installed

Outline: Data Analytics with R (IR)

Module 1: Language Basics

  • About data science
    • Data science definition
    • Process of doing data science.
  • Introducing R language
  • Variables and types
  • Control structures (loops and conditionals)
  • R scalars, vectors, and matrices
    • Defining R vectors
    • Matricies
  • String and text manipulation
    • Character data type
    • File IO
  • Lists
  • Functions
    • Closures
    • lapply/sapply functions
  • DataFrames

Module 2: 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

Module 3: Advanced Programming with R

  • Statistical Modeling with R
    • Statistical functions
    • Dealing with NA
    • Distributions (binomial, poisson, normal)
  • Regression
  • Recommendations
  • Text processing (tm package / wordclouds)
  • Clustering: KMeans
  • Classification
    • Naive bayes
    • Decision trees
    • Training using caret package
    • Evaluating algorithms
  • R and Big Data
    • Hadoop
    • Big Data ecosystem
    • RHadoop
Classroom Training

Duration 3 days

  • United States: US$ 2,500
Enroll now
Online Training

Duration 3 days

  • United States: US$ 2,500
Enroll now