Data Science with R Programming

Course level:All Levels
Course Duration: 55h 30m

About Course

We introduce R Programming for both beginners and professionals. In this course, you will get the basic and advanced concepts of data analysis and visualization using R Programming. In this course, you will learn, how you can program in R and how you can use R for effective data analysis. This course covers the installation and configuration of the required software in order to analyze complex and real-time data. During this course, you will get an opportunity to understand the practical issues in statistical computing that covers programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. You will learn all the topics of R such as introduction, features, installation, Rstudio ide, variables, data types, operators, if statement, vector, data handling, graphics, statistical modeling, etc.
Show More
Course Curriculum

What I will learn?

  • Course in the Hindi Language
  • Earn a sharable certificate upon completion
  • 100% online course
  • Complete the course according to your schedule
  • Get tangible career benefits after completion of the course
  • Approximately 55 hours of course content
  • No hard prerequisites

Course Curriculum

Introduction to R Programming

  • R Programming Language
  • Introduction to R
  • R as Programming Language
  • The need for R
  • R Installation
  • RStudio
  • RStudio UI
  • The Console
  • The Editor
  • The Environment Pane
  • The History Pane
  • The File Pane
  • The plots pane
  • The Package Pane
  • The Help Pane
  • The Viewer Pane

R Workspace Introduction
Introduction to R Workspace, R Working Directory, Create R Project in R Studio, Absolute and Relative Path, Managing the Project File

Inspecting an Environment
Topics Covered : Inspecting environment & symbols, View the structure of object, Removing Symbols

Modifying Global Options
Topics Covered : Modify Global Options, Modifying the number of digits, Modifying the warning level

Managing the library of packages
Topics Covered : Manage Package, Getting to know a package, Installing package from CRAN, Update package from CRAN, Install package from online repositories

Package Functions
Topics Covered : Package Function, Masking and name conflicts

R Basic Objects – Vector
Topics Covered - Basic Objects, Vector, Numeric Vector, Logic Vector, Character Vector, Sub setting Vectors, Named Vector, Extracting Elements, Class of the Vector, Converting Vectors, Arithmetic Operator

R Basic Objects – Matrix
Topics Covered - Matrix, Naming Rows and Columns, Subset a Matrix, Matrix Operator

R Basic Objects – Array
Topics Covered - What is Array, Subset of Array, Homogeneous vs Heterogeneous Array

R Basic Objects – List
Topics - List, Extracting Element, Subset a List, Named List, Setting Values, Other List Operations

R Basic Objects – Data Frame
Topics - Create Data Frame, Naming Row and Column, Subsetting Data Frame, Setting Values, Factors, Useful Functions for Data Frame, Loading and Writing Data to Disk

R Basic Objects – Functions
Topics - Create and Call a Function, Dynamic Typing, Generalizing a Function, Default Value

R Basic Expressions – Assignment Expressions
Topics - Basic Expressions, Assignment, Backtick

R Basic Expressions – Conditional Expressions
Topic - Conditional Expressions, Use if as a statement, Use if as an expression, Use if with vector, Vectorised if:ifelse, Switch function

R Basic Expressions – Loop Expressions
Topics - For Loops, For Loop - List & Data Frame, For Loop - Managing Flow, Nested For Loop, While Loop

R Built-in Functions – Basic Objects
Topics - Overview of Basic Objects, Testing Object Type, Accessing Object Classes and Type, Getting Data Dimension, Reshaping Data Structure, Iterating Over One Dimension

R Built-in Function – Logical Function
Topic - Logical Operators, Logical Functions, Which Elements are TRUE, NULL Values, Logical Coercion

R Built-In Function – Math Function
Topics - Math Functions, Number Rounding Functions, Trigonometric Functions, Hyperbolic Functions, Extreme Functions

R Built-in Function – Numeric Methods
Topics - Finding Roots, Derivatives, Integration

R Built-in Function – Statistical Functions
Topics - Statistical Functions, Sampling from a Vector, Probability Distributions, Summary Statistics, Covariance and Correlation

R Strings – String and Character Vectors
Topics - Overview, Character Vectors, Printing Strings, Concatenating String, Transforming Text, Counting Characters, Trimming White Space, Substring, Splitting Text, Formatting Text

R Strings – Regular Expressions
Topics - Working with Regular Expressions, Finding a string pattern, Using Groups to Extract Data

R Date – Time Vectors
Topics - Date and Date/Time, Parsing Text as Date, Parsing Text as Time, Formating Text as Date/Time, Formatting date/time to string

R Reading and Writing Data
Topics - Overview, Text format files, Importing data via RStudio, Importing data using built-in functions, Importing data using readr package, Reading Writing Excel Worksheets, Reading Writing Native Data File, Loading built-in datasets

R Data Visualization – Overview
Topics - Introduction

R Data Visualization – Scatter Plot
Topics - Basic Scatter Plot, Scatter Plot - Two Vectors, Customize Chart Element, Customize Point Style, Scatter Plot - Logical Condition, Scatter Plot - Two Data Sets, Customize Point Colors

R Data Visualization – Line Plot
Topics - Create Line Plot, Line Type and Width, Multi-period Line Plot, Line Plot with Points, Multi series chart with legend

R Data Visualization – Bar Chart
Topics - barplot(), Project NYCFlights - Part 1

R Data Vizualization – Pie Chart

R Data Vizualization – Histogram and Density Plot
Histogram, Project NYCFlights Part 2


Material Includes

  • 55 hours of digital content
  • On-demand videos
  • Assignments
  • Quizzes
  • Projects
  • Unlimited access for 1 year
  • Certification on completion


  • Understand R Programming Concepts
  • Learn how to use the R GUI called R studio
  • Different data structures and data types in R

Target Audience

  • School Students
  • Engineering Students
  • Management Students
  • Working Professionals
  • Faculty members and teachers