Title: Assessment: How Many Segments, Really?
Course: Advanced Data Science: A Graduate Course > Section: Module 6: Unsupervised learning: clustering and PCA

Assessment: How Many Segments, Really?

You are handed a customer dataset with genuine underlying structure, correlated noise columns, and non-spherical, unequally sized groups that will punish naive k-means. The marketing team asks a deceptively simple question: how many distinct customer groups actually exist, and how would you describe each one so we can target them? Your work is judged the way segmentation is really defended in industry practice: on cluster stability under bootstrap resampling, on agreement with the instructor’s private ground-truth labels that you never see, and on the rigor of your cluster-count argument. A confident answer with a weak justification scores worse than a cautious answer with strong evidence.

◆ The graded simulation for this module is the graded, per-student assessment for this module. The reproducible analysis, the agreement check against held-out labels, and the segment profiles are submitted here.

The interactive simulation is the next item in this module. Open it there.

What you will do, step by step:

1. Explore and clean the data, then standardize the features so no single high-variance column dominates the distance metric.
2. Reduce dimensionality with PCA, report cumulative variance explained, and decide how many components to carry forward.
3. Run at least two clustering algorithms (for example k-means and a density- or linkage-based method) so your conclusion does not depend on one method’s assumptions.
4. Sweep a range of k values and record internal indices (silhouette, gap statistic, within-cluster sum of squares) for each.
5. Assess stability by bootstrap resampling: recluster many resamples and measure how consistently pairs of points co-cluster.
6. Use t-SNE or a PCA scatter only as a visual sanity check, explicitly noting what you refuse to conclude from it.
7. Choose a number of clusters and defend it with converging evidence rather than a single plot.
8. Profile each final cluster on the original features and write a plain-language description marketing can act on.
9. Package everything so a peer can rerun it from a fixed random seed and reproduce your figures.

What to submit: a reproducible clustering analysis (notebook or script with a fixed seed), a stability assessment showing bootstrap co-clustering results across candidate k values, and a one-page segment description that states your defended cluster count and characterizes each segment for a non-technical marketing reader.

How it is graded:

Criterion | What strong work shows | Pts
Cluster stability | Bootstrap resampling shows the chosen solution is stable, with high and reported co-clustering consistency for the selected k relative to neighbors. | 15
Agreement with private labels | The recovered clustering aligns well with the instructor’s held-out ground truth (for example by adjusted Rand index), evidence the structure is real and not an artifact. | 15
Rigor of the count argument | The number of clusters is justified by converging evidence from stability, internal indices, and interpretability, not from a single elbow. | 14
Preprocessing and method choice | Features are standardized, PCA use is justified, and more than one algorithm is used so the conclusion is not method-dependent. | 14
Actionable segment descriptions | Each cluster is profiled on interpretable original features and described in language a marketer can use, with stated confidence. | 14
Reproducibility | Code runs top to bottom from a fixed seed and regenerates the reported figures and counts. | 14
Honesty about uncertainty | The submission is candid where evidence conflicts and does not overclaim a precise count the data cannot support. | 14
Total | 100

◆ Why this resists generative substitution: The instructor’s private ground-truth labels and the specific injected structure of this dataset exist nowhere online, so a pasted generic answer cannot match them; and because the count must be defended under bootstrap stability tied to this exact data, a plausible-sounding but unverified number scores poorly. The grade rewards the analytic process on the given data, which a language model cannot fabricate without actually running it.

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