Upon completion of this course, you will be able to:

• Understand R language fundamentals, including basic syntax, variables, and types.
• Create functions and use control flow.
• Read and write data in R.
• Work with data in R.
• Create and customise visualisations using ggplot2.
• Perform predictive analytics using R.

The following items describe the outline of the course:

Day 1:

Module 1: Introduction to Big Data Analytics

• What is Data?
• Why Data Collection is Important
• Types of Data
• What is Data Science?
• Characteristics of Big Data – The Three V’s of Big Data
• Big Data Analytics and its Types

Module 2: Data Analytics Lifecycle

• Data Analytics Lifecycle Overview
• Detailed Explanation on Data Analytics Lifecycle

Module 3: Basic Programming Terminologies

• Variables
• Constants
• Keywords
• Comments
• Syntax

Module 4: Getting Started with R

• What is R?
• Install R and RStudio
• Explore RStudio Interface (With Lab Exercises)

Module 5: Data Types in R

• Numbers
• Strings
• Vectors
• Matrix
• Arrays

Day 2:

Module 5: Data Types in R

• Data Frames
• Lists
• Factor (With Lab Exercises)

Module 6: Control Structures and Functions in R

• Conditional Statements
• Looping Statements
• Operators
• Functions Syntax
• Scoping Rules
• Subsetting
• Apply Functions (lapply, sapply, vapply)
• Debugging Tools
• Split Function

Module 7: Dealing with Date and Time in R

• Date Time Representation
• Date Time Arithmetic
• Date Time Comparison

Day 3:

Module 8: Data Gathering

• Reading Data from CSV File
• Reading Data from JSON File
• Reading Data from XML File
• Reading Data from Web

Module 9: Data Cleansing and Exploration

• Extract, Transform and Load (ETL)
• Data Cleansing
• Aggregation, Filtering, Sorting, Joining
• Dealing with Missing Data
• Selecting Columns and Rows
• Data Wrangling
• Summarise and Group By

Module 10: Simulation and Profiling

• Random Sampling
• Generate Random Numbers
• R Profiler

Module 11: Data Visualisation

• What Is Visualisation?
• Need of Visualisation
• Types of Visualisation
• How to Handle the Properties for Chart Creation
• Activity

Day 4:

Module 12: Getting Deeper into Data Visualisation

• Scatter Plots
• Boxplots
• Bar Charts
• Pie Charts
• Histograms

Module 13: Creating Graphs with ggplot

• Getting Started with ggplot
• Mapping Color, Shape and Size
• Creating Attractive Color Scheme
• Creating Bar Charts
• Creating Box Plots

Module 14: Advance Graphs in ggplot

• Correlation
• Deviation
• Ranking
• Distribution
• Composition
• Time Series Plots
• Groups
• Spatial

Day 5:

Module 15: Shiny R Package

• Introduction
• How to Build a Simple Shiny Module?

Final Evaluation (Exam)