training

 

Data Analytics with R – 3 Days

Course Description

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.

Intended Audience

This course is designed for developers and data analysts.

Requirements

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

Prerequisites

A basic programming background is preferred.

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

 

 

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