Week 2 - Cluster Analysis
Learning Objectives
After today's class, you should be able to:
- Understand and apply cluster analysis techniques, including the k-Means algorithm, to segment data into a set of homogeneous clusters of records for the purpose of generating insight.
- Create a new Conda environment and install required Python libraries.
- Use GitHub Desktop (or Git) to clone a repository and download starter Jupyter notebooks for in class demos.
- Develop and implement cluster analysis models in Python.
- Apply the k-Means algorithm manually to a small dataset to understand how cluster assignments and centroid updates occur in each iteration.
Class Agenda
- Cluster Analysis Slides
[5 min]Break- Cluster Analysis Python Demo
- In Class Time for CP1_Cluster_Analysis
Task List
- Optional Reading: Chapters 3-4 - Data Visualization and Dimension Reduction
- Required Reading: Chapter 16 - Cluster Analysis
- Complete and Submit CP1_Cluster_Analysis on Canvas.
- Complete and Submit PA1_Wine_Customer_Segmentation on Canvas/Gradescope.