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Data Science with R - Everything you need to know about Data Science and Machine Learning

This course will teach you how to program in R, how to analyse and visualize data, and how to use that data effectively. The course will cover the installation and configuration of software required for a statistical programming environment, as well as the implementation of programming language concepts in a high-level statistical language.

In this course, you will learn not only how to program in R, but also how to become a professional Data Scientist working with R.

The course covers practical aspects of statistical computing, including programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organising and commenting R code. With a combination of practical work and solid theoretical training, we take you through the basics of R Programming to mastery.

This course is suitable for anyone who has some coding experience or would like to learn more about the advanced features of the R programming language.

Enrol now to explore the world of R Programming Language!

After Completing The Course, You Will:

Course Design

You can take this Data Science with R Online Course at your own pace because it is designed to be flexible. There are multiple modules in the course, so that you can complete it according to your schedule. To make it easier for you to grasp the ideas, the topics are arranged in an orderly fashion. You will be assessed to ensure that you understand the material. You can access our courses on any smart device or through the web.


The Data Science with R Course features an online multiple-choice assessment test at the end of the course to assess learners’ ability and knowledge to understand the topics. This online multiple-choice test will result in an immediate grade, so you’ll know if you passed right away.

Certification & Transcript

After completing the course, you will be able to obtain* a Certificate of Completion from SkillArts.  This certificate will serve as proof that you have successfully completed the course. You will also be able to apply for an Academic Transcript that will outline the lessons covered in this Data Science with R Course. 

 If you obtain a certificate from us, you can include it in your portfolio of evidence and use it for interviews in an employment or academic environment. By scanning the QR code on the certificate and entering the certificate code on our website, your employer can verify the certificate.
*Prices apply

Example Certificate & Transcript

Career Opportunities

By providing you with all the necessary guidelines, knowledge, and an online certificate of completion, this Data Science with R Course will ease your way up the career ladder. You’ll learn how to succeed in your dream job, get that promotion, or start up the business you’ve always dreamed of.

Section 1: Introduction to Data Science +ML with R from A-Z

  1. Intro To DS+ML Section Overview
  2. What is Data Science?
  3. Machine Learning Overview
  4. Who is this course for?
  5. Data Science + Machine Learning Marketplace
  6. DS+ ML Job Opportunities 
  7. Data Science Job Roles

Section 2: Getting Started with R 

  1. Getting Started
  2. Basics
  3. Files
  4. R Studio
  5. Tidyverse
  6. Resources


Section 3: Data Types and Structures in R

  1. Section Introduction
  2. Basic Types
  3. Vectors Part One
  4. Vectors Part Two
  5. Vectors: Missing Values
  6. Vectors: Coercion
  7. Vectors: Naming
  8. Vectors: Misc.
  9. Matrices
  10. Lists
  11. Introduction to Data Frames
  12. Creating Data Frames
  13. Data Frames: Helper Functions
  14. Data Frames: Tibbles


Section 4: Intermediate R

  1. Section Introduction
  2. Relational Operators
  3. Logical Operators
  4. Conditional Statements
  5. Loops
  6. Functions
  7. Packages
  8. Factors
  9. Dates & Times
  10. Functional Programming
  11. Data Import/Export
  12. Databases


Section 5: Data Manipulation in R

  1. Section Introduction
  2. Tidy Data
  3. The Pipe Operator
  4. {dplyr}: The Filter Verb
  5. {dplyr}: The Select Verb
  6. {dplyr}: The Mutate Verb
  7. {dplyr}: The Arrange Verb
  8. {dplyr}: The Summarize Verb
  9. Data Pivoting: {tidyr}
  10. String Manipulation: {stringr}
  11. Web Scraping: {rvest}
  12. JSON Parsing: {jsonlite}


Section 6: Data Visualization in R

  1. Section Introduction
  2. Getting Started
  3. Aesthetics Mappings
  4. Single Variable Plots
  5. Two-Variable Plots
  6. Facets, Layering, and Coordinate Systems
  7. Styling and Saving


Section 7: Creating Reports with R Markdown

  1. Intro To R Markdown


Section 8: Building Webapps with R Shiny

  1. Intro to R Shiny
  2. A Basic Webapp
  3. Other Examples


Section 9: Introduction to Machine Learning

  1. Intro to ML Part 1
  2. Intro to ML Part 2


Section 10: Data Preprocessing

  1. Section Overview
  2. Data Preprocessing


Section 11: Linear Regression: A Simple Model

  1. Section Introduction
  2. A Simple Model


Section 12: Exploratory Data Analysis

  1. Section Introduction
  2. Hands-on Exploratory Data Analysis


Section 13: Linear Regression: A Real Model

  1. Section Introduction
  2. Linear Regression in R

Section 14: Logistic Regression

  1. Logistic Regression Intro
  2. Logistic Regression in R


Section 15: Starting a Career in Data Science

  1. Section Overview
  2. Creating A Data Science Resume
  3. Getting Started with Freelancing
  4. Top Freelance Websites
  5. Personal Branding
  6. Networking 
  7. Setting Up a Website