Unit 7: Meaningful Visualization and the Data Science Lifecycle

A data-science workflow is not a conveyor belt. Weak evidence should send you backward—to the question, the data, the preparation, or the analysis.

Source lessons

1. Making Meaningful Visualizations
2. Introduction to the Data Science Lifecycle

Read the visualization lesson as communication design and the lifecycle lesson as systems thinking. Every stage produces evidence and every stage can fail.

What you should be able to prove

By the end of the Unit, you should be able to:

• trace a question through acquisition, preparation, analysis, visualization, and communication;
• name the evidence produced at each stage;
• identify failure modes that should trigger revision; and
• place ethics, privacy, and bias checks inside the workflow rather than at the end.

Evidence check

Finish with Evidence Check — Trace the Lifecycle. Your artifact is a lifecycle trace with evidence, failure, and decision points.

Runtime boundary

No cloud account is required. Use a public, fictional, or teacher-supplied scenario.