R for Data Analysis: The Ultimate Beginner's Guide

An 80-20 Approach to Proficiency for Beginners, taught by a Ph.D. Data Scientist

This is the R course for beginners with no coding experience. It is based on the latest research in online learning theory and my personal experience with dozens of online courses. I created this course as the course I wish I would have had when I first started learning R.

We will code together and focus on the 20% of code responsible for 80% of the work. At the end of sections, you will have a 'Make It Stick' challenge to apply what you have just learned with a different dataset (based on principles in the book 'Make It Stick').
This course is different from other beginner courses in R in a couple significant ways:
Project-based learning with real-world scenarios: All lessons are based on common questions facing data practioners.
Content focus: The course outline and lectures are based on everyday workflows of data practioners rather than a bottom-up approach to R programming. Practically, this means we won't spend much time learning about R and core principles of programming; we will immediately start with how you will use it.
Current (& continually updated) code: I work in R everyday and make sure you are learning the best and most efficient ways to accomplish the most common and important tasks. For example, the rowwise function in the dplyr package enables you to perform calculations across columns by rows. A single line of code can now accomplish what was previously far more challenging.
Keeping it real: I keep the video rolling when I make an error. You can learn a lot from mistakes. R was my first programming language and I quit twice because of too many errors, too much time to learn it, and frustration with online courses that left out important steps or assumed knowledge that simply wasn't there. I try really hard to explain what we're doing while we're doing it and then giving you an opportunity to do it on your own with a different (but related) dataset.   
 

In this course, you will learn to:

• Load data from different sources (files, databases)

• Structure data for analysis using the tidyverse packages

• Quickly explore and visualize data trends

• Conduct feature engineering for deeper analysis

• Analyze survey data

• Select the right visualization for your data

• Create professional visualizations

• Create and automate reports using RMarkdown