Title: Overview
Course: Advanced Data Science: A Graduate Course > Section: Module 6: Unsupervised learning: clustering and PCA

Module 6: Unsupervised learning: clustering and PCA

Overview

This module moves from supervised prediction to the harder question of what structure exists in data when no labels are given. You will apply principal component analysis to compress correlated features into interpretable axes of variance, and apply clustering to propose groupings, and then you will do the part that separates a data scientist from a button-pusher: defend those choices with evidence rather than assertion. The recurring theme is that unsupervised results are hypotheses, not answers, so every claim about “how many groups there are” must be earned through stability, internal validity, and interpretability. This module serves program outcomes O3 (differentiate among machine-learning approaches, including clustering, and select one appropriate to the problem and its data) and O5 (communicate findings to a non-technical decision-maker through sound visualization and a defensible data narrative).

By the end of this module

- Apply PCA to a standardized feature matrix and read the loadings, scree plot, and cumulative variance explained to interpret what each component represents.
- Run k-means, explain Lloyd’s algorithm step by step (assignment then update, convergence to a local optimum), and know why initialization and standardization matter.
- Argue for a specific number of clusters using converging evidence, such as bootstrap stability, silhouette width, and the gap statistic, instead of eyeballing an elbow.
- Use t-SNE as an exploratory visualization while resisting over-reading it, correctly treating perplexity as a tuning knob and refusing to interpret cluster size or distance.
- Communicate a segmentation result to a non-technical stakeholder, stating both what the segments are and how confident you are that they are real.

Readings

- An Introduction to Statistical Learning with Python (ISLP) (free to read · link), Chapter 12 on unsupervised learning with its PCA and clustering labs. Focus on the geometry of principal components and the proportion of variance explained, then work the k-means and hierarchical clustering labs end to end, paying attention to why the authors standardize variables before clustering.
- Harvard CS109B clustering notebooks (MIT · adapt). Focus on the notebooks that compute silhouette scores and compare clustering across k, and adapt their bootstrap or resampling patterns for the summative. Use ONLY these URLs.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”