Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

Video description

The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you’ll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

What You Will Learn

  • Learn data cleaning, processing, wrangling, and manipulation
  • Learn plotting in R (graphs, charts, plots, histograms, and more)
  • How to create a resume and land your first job as a data scientist
  • Learn machine learning and its various practical applications
  • Learn data and file management in R
  • Use R to clean, analyze, and visualize data


This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.

About The Author

Juan E. Galvan: Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.

Table of contents

  1. Chapter 1 : Data Science and Machine Leaning Course Introduction
    1. Data Science and Machine Learning Introduction Section Overview
    2. What is Data Science?
    3. Machine Learning Overview
    4. Data Science + Machine Learning Marketplace
    5. Who is this Course For?
    6. Data Science and Machine Learning Job Opportunities
    7. Data Science Job Roles
  2. Chapter 2 : Getting Started with R
    1. Getting Started with R
    2. R Basics
    3. Working with Files
    4. R Studio
    5. Tidyverse Overview
    6. Additional Resources
  3. Chapter 3 : Data Types and Structures in R
    1. Data Types and Structures in R Section Overview
    2. Basic Types
    3. Vectors - Part One
    4. Vectors - Part Two
    5. Vectors: Missing Values
    6. Vectors: Coercion
    7. Vectors: Naming
    8. Vectors: Miscellaneous
    9. Working with Matrices
    10. Working with Lists
    11. Introduction to Data Frames
    12. Creating Data Frames
    13. Data Frames: Helper Functions
    14. Data Frames: Tibbles
  4. Chapter 4 : Intermediate R
    1. Intermedia R Section Introduction
    2. Relational Operators
    3. Logical Operators
    4. Conditional Statements
    5. Working with Loops
    6. Working with Functions
    7. Working with Packages
    8. Working with Factors
    9. Dates and Times
    10. Functional Programming
    11. Data Import/Export
    12. Working with Databases
  5. Chapter 5 : Data Manipulation in R
    1. Data Manipulation 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}
  6. Chapter 6 : Data Visualization in R
    1. Data Visualization in R Section Introduction
    2. Getting Started with Data Visualization in R
    3. Aesthetics Mappings
    4. Single Variable Plots
    5. Two Variable Plots
    6. Facets, Layering, and Coordinate Systems
    7. Styling and Saving
  7. Chapter 7 : Creating Reports with R Markdown
    1. Introduction to R Markdown
  8. Chapter 8 : Building Webapps with R Shiny
    1. Introduction to R Shiny
    2. Creating a Basic R Shiny App
    3. Other Examples with R Shiny
  9. Chapter 9 : Introduction to Machine Learning
    1. Introduction to Machine Learning Part One
    2. Introduction to Machine Learning Part Two
  10. Chapter 10 : Data Preprocessing
    1. Data Preprocessing Introduction
    2. Data Preprocessing
  11. Chapter 11 : Linear Regression: A Simple Model
    1. Linear Regression: A Simple Model Introduction
    2. A Simple Model
  12. Chapter 12 : Exploratory Data Analysis
    1. Exploratory Data Analysis Introduction
    2. Hands-on Exploratory Data Analysis
  13. Chapter 13 : Linear Regression - a Real Model
    1. Linear Regression - Real Model Section Introduction
    2. Linear Regression in R - Real Model
  14. Chapter 14 : Logistic Regression
    1. Introduction to Logistic Regression
    2. Logistic Regression in R
  15. Chapter 15 : Starting a Career in Data Science
    1. Starting a Data Science Career Section Overview
    2. Creating a Data Science Resume
    3. Getting Started with Freelancing
    4. Top Freelance Websites
    5. Personal Branding
    6. Networking Do's and Don'ts
    7. Setting Up a Website

Product information

  • Title: Data Science and Machine Learning with R from A-Z Course [Updated for 2021]
  • Author(s): Juan E. Galvan
  • Release date: April 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801075282