Are you curious about clustering? Are you looking for an intuitive, approachable way to teach your students machine learning? Our newest case uses Excel to help business students with no prior experience with data analytics learn about k-means clustering. The case includes a full instructor guide, solution, and step-by-step solution video, so you can jump right in and teach.
In addition, join the case creator on Thursday, April 28, at 4 pm CT in a live webinar to discuss how to implement and teach the case.
We are grateful to Ron Guymon, Senior Lecturer of Accountancy and RC Evans Data Analytics Fellow, for authoring this content.
Overview of the case
This case is an excellent opportunity to introduce the idea of unsupervised learning and motivate the practicality of a data analytic language. The case helps business students with no prior experience with data analytics learn about k-means clustering. It also allows instructors with no experience to teach machine learning.
The objectives of the case are to:
Build a conceptual foundation for performing k-means clustering in a business setting.
Introduce students to a programming language and integrated development environment, which can be used to automate machine learning tasks.
Gain expertise using k-means clustering by practicing cluster interpretation and by exploring the effect of scaling variables on cluster formation.
This content includes the following components:
A video file, Word document, and PowerPoint deck for the instructor demonstrating how to teach this lesson and how to use the material
Case solution for the instructor
Case and case materials for the student
Why Machine Learning?
Haven't you heard, artificial intelligence is taking over the world. Well, maybe not yet, but even the smallest businesses are looking to leverage machine learning. At its core, ML is all about learning quickly and automatically from your data. Our students need an understanding of how this works because they cannot avoid it when they enter the business world.
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