New Cheat Sheet - Customer Segmentation and Clustering Workflow

Written by Matt Dancho

Quite often our student feedback leads to BIG IMPROVEMENTS in our courses and big developments that help everyone! This is a perfect example. Based on student feedback, we’ve completely upgraded the Week 6 materials in our foundational Business Analysis with R Course creating a BRAND NEW CHEAT SHEET on Customer Segmentation in the process. We’ve open sourced the NEW CHEAT SHEET, which is available for download below. Read on to find out why we created the cheat sheet, how it works, and how it helps you.

Customer Segmentation Cheat Sheet - Page 1

Learn how one student's feedback led to the creation of a new Cheat Sheet that benefits everyone.

Here’s what this article has for you:

  1. Creation of the Cheat Sheet - How a student spoke and we listened
  2. Student feedback led to a MAJOR UPGRADE to our Business Analysis with R course
  3. Free Resources - Download the New Cheat Sheet
  4. SPECIAL OFFER - 15% OFF until March 1

Creation of the Cheat Sheet - How a Student Spoke and We Listened

As a course instructor and creator, there is nothing more valuable than constructive feedback from our students. This helps big time. Here’s a prime example of what one student said, and how it improved the course.

Last, week I released the first part of the Modeling section in our Business Analysis with R course, which covers Customer Segmentation with K-Means and UMAP - 2 important techniques for data mining based on customer similarity. It’s a nerve-wracking experience launching new material, and the initial feedback is critical to understanding if I hit the mark.

“This Week Just Felt Different”

One of my students contacted me. His point was that the modeling section didn’t match the feel of the previous weeks. He loved how weeks 1 through 5 used Cheat Sheets to explain subjects building on each other, and how they were interwoven into the lessons. He loved how the key concepts built on each other.

He said, “this week just felt different”.

For me, the course creator, this is something that I really wanted to address, especially if my goal is to truly make the #1 most effective applied data science education.

Digestion, Reflection, and Solution!

I took this week to digest the comment and get to the root of the problem. After speaking with the student over our Slack Channel, we nailed down the problem.

The student needed me to make the process repeatable. He said, “I valued the content, I just don’t think I could apply it”. Bingo!

Now that I understood the problem, I needed to figure out a solution with his input. What we decided to do was:

  1. Create a new Cheat Sheet (Segmentation and Clustering Workflow) that covered the high-level concepts, summarized popular algorithms (K-Means, Hierarchical Clustering, PCA, tSNE, UMAP), code examples, and most importantly a 2nd page that covers the step-by-step workflow for applying clustering to segment customers/companies.

  2. Create a new Challenge (Company Segmentation using Stock Prices) that reinforced the learning where the student applies the material. We settled on an exciting Company Segmentation Project using Stock Prices to cluster similar companies based on their stock price movement.

What did our student think?

Student Feedback

And here’s what he is referring to - Page 2 that summarizes the workflow.

Cheat Sheet - Customer Segmentation - Page 2

Customer Segmentation Cheat Sheet - Page 2 - The Workflow

The input from our student was critical. Sometimes we miss the mark, and we need to course correct.

Student Feedback led to a Major Upgrade to our Business Analysis with R Course

We made a major upgrade to the Business Analysis with R course - New in Modeling: Week 6:

  1. We added the new cheat sheet with a 10-Minute Overview of Clustering and Dimensionality Reduction techniques

  2. We integrated the new cheat sheet into all of the course videos

  3. We added this new challenge with 30-Minute solution - Using Stock Prices to Segment Companies - Watch the 1-minute Video


  • 1 New Cheat Sheet
  • 1 New Stock Price Challenge - Will take about 2 hours to complete
  • 7 new videos
  • 15 upgraded videos

All for you - the students! :)

1-minute overview of the new Stock Price Clustering Challenge

Watch the New Stock Price Clustering Challenge - 1-Minute Overview

This combination of strategic learning, application of value-adding techniques, and cutting-edge learning resources is how you will learn R for business fast.

Free Resources - Download the New Cheat Sheet

We didn’t stop at upgrading the course - We open-sourced the new Cheat Sheet so everyone can benefit!

My goal is to help everyone learn - you get free resources!

The new cheat sheet covers:

  • Summary of how data science can be used for segmentation and clustering
  • What algorithms exist and what they are used for
  • Code examples in R and Python
  • MOST IMPORTANT - A repeatable workflow for implementing customer or company segmentation

Here’s the 10-Minute Overview on how to use the cheat sheet from the course (important - you need to know understand how it works!).

10-Minute Overview of Segmentation and Clustering Workflow

And, here is the cheat sheet for available download.

Cheat sheets are continuously evolving and improving. Sign up to stay updated.

Don’t forget to sign up for Cheat Sheet updates. We just updated our Ultimate R Cheat Sheet.

The resources are great! But, even better is learning how to apply the resources because these tools will accelerate your career. That's why I'm excited to provide course savings.

Course Savings - Now is the time to accelerate your career!

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Here’s a 2-minute Course Overview of what you are getting with Business Analysis with R - Our cutting-edge course that integrates the new cheat sheet as one of the 20+ data science resources you will learn.

Course Overview - Business Analysis with R

More Course Savings

Limited Time Offer

Expires March 1st, 2019

We just turned 2 this month, and we are extending savings to you. We are committed to helping you succeed. Here are several options to learn data science fast and effectively.

I look forward to having you in my courses. I will do everything in my power to help you succeed.

Matt Dancho, Business Science University Instructor