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Udacity

Segmentation and Clustering

via Udacity

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 Fundamentals
    • Understand the difference between localization, standardization, and segmentation
  • Preparing Data for Clustering
    • Scale data to prepare a dataset for cluster modeling. Select variables to include based on the business context.
  • Variable Reduction
    • Use principal components analysis (PCA) to reduce the number of variables for cluster model
  • Clustering Models
    • Select the appropriate number of clusters. Build and apply a k-centroid cluster model.
  • Validating and Applying Clusters
    • Validate the results of a cluster model. Visualize and communicate the results of a cluster model.

Taught by

Rod Light

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