Learn Data Science and Machine Learning with R from A-Z

Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

15 sections • 81 lectures • 28hrs 49mins
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Learn Data Science and Machine Learning with R from A-Z
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Welcome to the Learn Data Science and Machine Learning with R from A-Z Course!

In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.

The course covers practical issues in statistical computing which 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 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.

Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.

Audience: Consultants, Developers, End users, IT/Business Analysts, Project team members, system administrators
Prerequisites: Basic computer skills

What you'll learn/Goals:

  1. Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
  2. How to write complex R programs for practical industry scenarios
  3. Learn data cleaning, processing, wrangling and manipulation
  4. Learn Plotting in R (graphs, charts, plots, histograms etc)
  5. How to create resume and land your first job as a Data Scientist
  6. Step by step practical knowledge of R programming language
  7. Learn Machine Learning and it's various practical applications
  8. Building web apps and online, interactive dashboards with R Shiny
  9. Learn Data and File Management in R
  10. Use R to clean, analyze, and visualize data
  11. Learn the Tidyverse
  12. Learn Operators, Vectors, Lists and their application
  13. Data visualization (ggplot2)
  14. Data extraction and web scraping
  15. Full-stack data science development
  16. Building custom data solutions
  17. Automating dynamic report generation
  18. Data science for business

Course content
15 sections • 81 lectures • 28hrs 49mins

Data Science ML Course Intro - 02mins
What is data science - 09mins
Machine Learning Overview - 05mins
Who's this course is for - 02mins
DL and ML Marketplace - 04mins
Data Science and ML Job opportunities - 02mins
Data Science Job Roles - 04mins
Getting Started - 10mins
Basics - 06mins
Files - 11mins
RStudio - 06mins
Tidyverse - 05mins
Resources - 04mins
Section Introduction - 30mins
Basic Types - 08mins
Vectors Part One - 19mins
Vectors Part Two - 24mins
Vectors - Missing Values - 15mins
Vectors - Coercion - 14mins
Vectors - Naming - 10mins
Vectors - Misc - 05mins
Creating Matrices - 31mins
Lists - 31mins
Introduction to Data Frames - 19mins
Creating Data Frames - 19mins
Data Frames and Helper Functions - 31mins
Data Frames - Tibbles - 39mins
Section Introduction Intermediate R - 46mins
Relational Operations - 11mins
Logical Operators - 07mins
Conditional Statements - 11mins
Loops - 07mins
Functions - 14mins
Packages - 11mins
Factors - 28mins
Dates and Times - 30mins
Functional Programming - 36mins
Data Import or Export - 22mins
Database - 27mins
Data Manipulation in R Section Introduction - 36mins
Tidy Data - 10mins
The Pipe Operator - 14mins
The Filter Verb - 21mins
The Select Verb - 46mins
The Mutate Verb - 31mins
The Arrange Verb - 10mins
The Summarize Verb - 23mins
Data Pivoting - 42mins
JSON Parsing - 10mins
String Manipulation - 32mins
Web Scraping - 58mins
Data Visualization in R Section Introduction - 17mins
Getting Started - 15mins
Aesthetics Mappings - 24mins
Single Variables Plot - 36mins
Two Variable Plots - 20mins
Facets Layering and Coordinate System - 17mins
Styling and Saving - 11mins
Creating-Reports-with-R-Markdown - 28mins
Section-Introduction-With-R-Shiny - 26mins
A Basic App - 31mins
Other Examples - 34mins
Intro to Machine Learning - Part 1 - 21mins
Intro to Machine Learning - Part 2 - 46mins
Introduction to Data Preprocessing - 27mins
Data Preprocessing - 37mins
LR Section Introduction - 25mins
Linear Regression A Simple Model - 53mins
Section Introduction EDA - 25mins
Hands-on Exploratory Data Analysis - 01hrs 02mins
Linear Regression - Real Model Section Intro - 32mins
Linear Regression in R - real model - 52mins
Introduction to Logistic Regression - 37mins
Logistic Regression in R - 39mins
Starting a Career in Data Science1 - 02mins
Data Science Resume - 03mins
Getting Started with Freelancing - 04mins
Top Freelancing Websites - 05mins
Personal Branding - 05mins
Importance of Website and Blog - 03mins
Networking do's and dont's - 03mins

The course covers 6 main areas:

This intro section gives you a full introduction to the R programming language, data science industry and marketplace, job opportunities and salaries, and the various data science job roles.

  1. Intro to Data Science + Machine Learning
  2. Data Science Industry and Marketplace
  3. Data Science Job Opportunities
  4. R Introduction
  5. Getting Started with R


This section gives you a full introduction to the data types and structures in R with hands-on step by step training.

  1. Vectors
  2. Matrices
  3. Lists
  4. Data Frames
  5. Operators
  6. Loops
  7. Functions
  8. Databases + more!

This section gives you a full introduction to the Data Manipulation in R with hands-on step by step training.

  1. Tidy Data
  2. Pipe Operator
  3. dplyr verbs: Filter, Select, Mutate, Arrange + more!
  4. String Manipulation
  5. Web Scraping

This section gives you a full introduction to the Data Visualization in R with hands-on step by step training.

  1. Aesthetics Mappings
  2. Single Variable Plots
  3. Two-Variable Plots
  4. Facets, Layering, and Coordinate System

This section gives you a full introduction to Machine Learning with hands-on step by step training.

  1. Intro to Machine Learning
  2. Data Preprocessing
  3. Linear Regression
  4. Logistic Regression
  5. Support Vector Machines
  6. K-Means Clustering
  7. Ensemble Learning
  8. Natural Language Processing
  9. Neural Nets

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

  1. Creating a Resume
  2. Personal Branding
  3. Freelancing + Freelance websites
  4. Importance of Having a Website
  5. Networking

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

Juan E. Galvan

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.

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