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Module 10 · Project 6 · Due the date on Canvas · PCA & Dimension Reduction · adaptive competency DV18

Rotate the data. Reveal the structure.

Turn a cloud of correlated variables into defensible principal components—without treating PCA as a mysterious R command.

What to do for Canvas Project 6

Work through Module 10 and save its required evidence and files.
Complete the associated workspaces: Module 10, Module 11, Module 12. Previously earned completion remains valid; additional work is optional review.
Open Project 6, check the packet status, and download Project_6_Canvas_Packet.zip. For Canvas, upload at least one artifact from your work or a screenshot of your grade. The full ZIP is optional. A download is not a Canvas submission. Submit by the date on Canvas, only if you have not already submitted.

QuantegyAI feedback does not submit to Canvas. Module numbers, evidence identifiers, and Canvas weekly resource numbers are distinct.

9module steps

4interactive challenges

1PCA evidence defense

60+minutes

Mission progress0%

Begin with the five-minute linear-algebra refresh.

Begin PCA mission

By the end of this module, you will be able to

Refresh the matrix foundations PCA runs on: standardization and covariance structure.
Explain why a scaling decision changes the components you get.
Rotate and interpret components: read loadings and name what each component measures.
Verify a PCA in browser R and defend which components to keep.
Audit AI PCA output for invented or uninterpretable structure.
Apply all of this to Project 6: your DV18 PCA Evidence Studio, due the date on Canvas.

Your evidence map

DV18

0 XP

DV18Use vectors and projectionsReady to begin

DV18Center and standardizeLocked

DV18Maximize projected varianceLocked

DV18Interpret loadings and scoresLocked

DV18Verify PCA in RLocked

DV18Defend what PCA preservesLocked

Student learning outcomes

By the end of Module 10, students can…

Explain component scores as projections — dot products of centered data onto a unit direction.
Decide when to standardize before PCA and demonstrate the scale effect that raw units create.
Connect covariance matrices, eigenvectors, and eigenvalues to the maximum-variance direction.
Interpret loadings, scores, scree plots, and explained variance without causal or construct overreach.
State what a reduced view omits and defend the retained-component choice in writing.

Module 10 step by step

Each step below is its own short page, so nothing runs on for screens at a time. Work through them in order — your progress is saved as you go, and the numbered bar at the top of every page jumps straight to any step.