Segmentation and Clustering

Overview

The Segmentation and Clustering course provides students with the foundational knowledge to build and apply clustering models to develop more sophisticated segmentation in business contexts. You will learn:

The key concepts of segmentation and clustering, such as standardization vs. localization, distance, and scaling
The concepts of variable reduction and how to use principal components analysis (PCA) to prepare data for clustering models
How to choose between hierarchical and k-centroid clustering models
How to build and apply k-centroid clustering models

Throughout this course you’ll also learn the techniques to apply your knowledge in a data analytics program called Alteryx.

This course is part of the Business Analyst Nanodegree Program.

Syllabus

Segmentation and Clustering Fundamentals

Learn the difference between standardization and localization.,Learn about the concept of distance in clustering models.,Get introduced to how segmentation is used in business.

Data Preparation for Clustering Models

Learn how to select data for clustering models.,Learn what data types can be used in clustering models.,Scale and transform data for clustering models.

Variable Reduction

Learn the difference between factor analysis and principle components analysis.,Learn to use principal components analysis to reduce the number of variables in a model.

Clustering models design

Learn the difference between k-centroid and hierarchical clustering models.,Be able to select the number of clusters for a k-centroid model.,Validate your clusters in Alteryx.

Lesson 5 – Building a Clustering Model

Build a k-centroid clustering model to segment retail stores.,Learn how to visualize and validate your clusters.,Be able to interpret the results and communicate the “story” of the analysis.