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

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