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

Practice and formative deliverable

The practice items below are formative and ungraded. They build intuition before the graded assessment. The formative deliverable is checked for completion and returned with feedback, and it earns completion points on submission, credited on completion rather than scored for correctness.

Build intuition first by manipulating each method interactively, then run a short reproducible analysis on a small dataset of your choice.

1. Open Setosa PCA, and drag the points to feel how the principal components rotate to follow the direction of greatest variance and how correlation shows up as a dominant first component.
2. Open Visualizing K-Means Clustering, and step through Lloyd’s algorithm by hand: watch the assignment and update steps alternate, try a bad initialization, and see it settle into a poor local optimum.
3. Open How to Use t-SNE Effectively, and move the perplexity sliders until you have personally reproduced a misleading picture, so you never trust a single t-SNE plot again. Use ONLY these URLs.

◎ Formative deliverable: On a small standardized dataset, run PCA and report the variance explained by the first two components, then run k-means, choose k using one explicit stability or silhouette argument, and write two sentences defending your k. Checked by the instructor against a short checklist (standardization applied, a named evidence source for k, and a defensible claim), returned with brief written feedback before the summative is due.

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