Segmentation and Clustering

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Skills Covered: Variable Reduction, Clustering Models Design, Alteryx, Variable Reduction, Principal Components Analysis (PCA)

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ABOUT THIS COURSE


In the Segmentation and Clustering course, you will gain the foundational knowledge to build and apply clustering models to develop more sophisticated segmentation in business contexts. You will be able to apply the key concepts of segmentation and clustering, such as standardization vs. localization, distance, and scaling as well as concepts of variable reduction and how to use principal components analysis (PCA) to prepare data for clustering models. You will also learn how to choose between hierarchical and k-centroid clustering models and how to build and apply k-centroid clustering models. This course will provide you with the techniques to apply your knowledge in a data analytics program called Alteryx.


This course is part of the Business Analyst Nanodegree Program.

 

WHAT YOU WILL LEARN


Segmentation and Clustering Fundamentals

  • Difference between standardization and localization.
  • Concept of distance in clustering models.
  • Get introduced to how segmentation is used in business.

Data Preparation for Clustering Models

  • How to select data for clustering models.
  • 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

  • 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.

Building a Clustering Model

  • Build a k-centroid clustering model to segment retail stores.
  • How to visualize and validate your clusters.
  • Interpret the results and communicate the “story” of the analysis.

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Segmentation and Clustering
Segmentation and Clustering

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