R Programming for Data Science

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Course Objectives

The objective of this course is to introduce participants to the data analytics with R
programming language, which is a widely used and up-coming statistical programming
language. In this course, participants will be exposed to data manipulation with R,
produce visualization for data exploration, use some common statistical and machine
learning methods to do predictive modelling and finally generate reproducible reports.

Description

By the end of the class, students learn to:

  • Utilize RStudio, understand R documentation and write R scripts
  • Acquire and manipulate data
  • Produce basic statistical summaries of the data
  • Apply statistical and machine learning models for data analysis and predictive modelling
  • Produce visualization using basic graphics functions and experience the ggplot2 packages
  • Produce reports in R Markdown

Target Audience

This course is suggested for programmer who wishes to get data into R, get it into the most useful structure, transform it, visualise it and model it.

Training Outline

Day 1
  • Overview of R and data analytics
  • Installing and setting up the R Environment
  • R data types and objects (vectors, lists, matrix and data frame)
  • Reading and writing data
  • List, matrix and data frame
  • Sub-setting data
  • Laboratory exercise – Working with Data Frame
Day 2
  • Control structures
  • Creating and using functions
  • Scoping rules, manipulating dates and times
  • Laboratory exercise – Working with dates
  • Using the R “apply” functions
  • Profiling in R
  • Laboratory exercise
Day 3
  • Concept of Tidy data in R
  • Importing data
  • Data Preprocessing with R
  • tidyr and dplyr
  • Introduction to data.table
  • Laboratory Exercise
  • Laboratory Exercise
Day 4
  • Introduction to base plotting system
  • Laboratory Exercise – base plots
  • Introduction to ggplot
  • Laboratory Exercise – ggplot
  • Introduction to Machine Learning and Caret Package I
  • Laboratory Exercise
Day 5
  • Introduction to Machine Learning and Caret Package II
  • Introduction to Shiny I
  • Introduction to Shiny II
  • Wrap up

Prerequisite

Basic knowledge of programming is preferable.