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Certified R Programmer for Data Science (CRPDS)

Course outline

5 days
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Duration

5 days

Overview

R programming language that is well-known for its power in statistical computing. The use of R in Data Science enables insights from data to be extracted, and these insights will allow you to get ahead of your competitors in no time.

In our Certified R Programmer for Data Science course, you will learn how to program and use R for effective data analysis. The course covers practical issues in statistical computing which includes reading data into R, accessing R packages, manipulation and visualization of data.

To get certified as an R programmer, you’ll also be exposed to a wide range of topics including Big Data, Data Analytics lifecycle and topics in statistical data analysis that will provide working examples.

What you will gain:

  • Understand R language fundamentals, including basic syntax, variables, and types.
  • Use R to clean, analyze, and visualize data.
  • Create and customise visualisations using ggplot2.
  • Perform predictive analytics using R.
  • Navigate the entire data science pipeline from data acquisition to publication.

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)
  • Comments
  • Syntax

Module 5: Data Types in R

  • Numbers
  • String
  • Vector
  • Matrix
  • Arrays

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

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

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

Introduction

  • How to Build a Simple Shiny Module?

Final Evaluation (Exam)

Programme scope, delivery arrangements and suitability are confirmed during enquiry.