Title: Practice and formative deliverable
Course: Advanced Data Science: A Graduate Course > Section: Module 3: Exploratory analysis, visualization, and the stakeholder story

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.

You will move from raw distributions to a defended encoding choice, then confront how easily the same data can be made to lie.

1. Run interactive EDA with Google Facets: load a dataset, sort features by distribution, and surface missing, constant, or skewed values, noting at least three features whose shape would change how you visualize them.
2. Choose an encoding with the Data-to-Viz chart chooser, working from your question and data type to a defended chart form, then study its misleading-figure catalog and identify which two caveats most threaten your chosen figure.
3. Pick a final encoding by communicative intent with the Financial Times Visual Vocabulary, matching your intent (comparison, change over time, part-to-whole, distribution) to a specific chart and justifying why it beats the runner-up. Use ONLY these URLs.

◎ Formative deliverable: Produce one honest figure and one deliberately misleading version of the same data (for example, by truncating the axis or switching to an unordered color scale), and write two sentences naming exactly what makes the difference between them. Self-check: confirm the honest version’s ink is proportional to the quantity and its title states a takeaway a reader could act on; confirm you can name the specific perceptual rule the misleading version breaks.

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