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Advanced Analytics with R training material delivered in a 2 day format
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Advanced R

Course Description

Welcome to Advanced Analytics with R! This short course builds onto the Intro to R and Intermediate R courses and provides a hands-on approach to applying advanced analytic techniques with the R programming language. In this course you will gain deeper knowledge around the analytic modeling process and apply various supervised and unsupervised machine learning algorithms. The following is an outline of the material covered in this training:

Day 1

Topic Time
Breakfast / Social time 8:00 - 9:00
Getting started 9:00 - 9:45
Unsupervised modeling 9:45 - 10:45
Break 10:45 - 11:00
Supervised modeling process 11:00 - 12:00
Lunch 12:00 - 1:00
Feature & target engineering 1:00 - 2:00
Regression & cousins 2:00 - 4:00
Q&A 4:00 - 4:30

Day 2

Topic Time
Breakfast / Social time 8:00 - 9:00
Decision trees, bagging, & random forests 9:00 - 11:00
Gradient boosting machines 11:00 - 12:00
Lunch 12:00 - 1:00
Stacked models & auto ML 1:00 - 2:00
Interpretable machine learning 2:15 - 3:45
Q&A 4:00 - 4:30

Schedule is still being refined and is subject to change; however, the topics should remain the same.

Course Preparation

To prepare for this course please complete the following prior to the day of class:

  1. Download the class material here. This will provide you with the R scripts and notebooks to follow along during class.

  2. After downloading the files, open the 00-run-this-script-first.R and run the code. This will make sure you have all required packages that will be used in the class.

  3. All slides are available via the hyperlinks in the schedule above so that you can follow along.

  4. Please have the following versions of R and RStudio installed. If you have an earlier version of R that is at least version 3.4.5 or later you should be ok but its best to be as current as possible.

  5. This course makes strong assumptions about your prior knowledge such as your ability to define functions, manage R objects, control the flow of a program, and other basic tasks. To ensure your success, be sure that you have reviewed and are comfortable with the material covered in the Intro to R and Intermediate R courses.

If you have any specific questions prior to the class you can reach out to me (Brad Boehmke) directly at

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