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

Module 3: Exploratory analysis, visualization, and the stakeholder story

Overview

This module is where analysis stops being for you and starts being for someone else. You will explore an unfamiliar dataset with discipline, and then compress what you found into a defensible, honest narrative that a non-technical decision-maker can act on inside five minutes. The intellectual move is the compression: deciding what to leave out without lying by omission, choosing an encoding because it serves the decision rather than because it looks impressive, and stating what you do not know in plain words. The module serves outcome O5 (communicate findings to a non-technical decision-maker through sound visualization and a defensible data narrative) and sets up O3 (select a method appropriate to a problem and its data), since disciplined exploration is what tells you which method the data can support.

By the end of this module

- Run principled exploratory analysis on an unfamiliar dataset: profile distributions, missingness, and skew, and separate signal worth reporting from noise and artifact.
- Choose a visual encoding by communicative intent, matching the chart form to the specific question the audience is asking rather than to the shape of the data alone.
- Spot and avoid misleading figures, including truncated or dual axes, disproportionate ink, and color that implies order or emphasis the data does not support.
- Tell a decision-focused data story: title every figure with its plain-language takeaway and state the residual uncertainty in words a VP can repeat.
- Defend an analysis live under skeptical, unscripted questioning, including answering “what would change your mind?” honestly.

Readings

- Fundamentals of Data Visualization (Wilke) (CC BY-NC-ND · link). Read Ch.4 on color, focusing on when to use sequential versus qualitative scales and why unordered rainbow palettes mislead; then Ch.17 on proportional ink and the ways truncated and transformed axes distort comparison. Focus on the worked before-and-after figures and be able to name, for each, the specific perceptual rule being violated.
- A structured critique of a live stakeholder dashboard: pick one current visualization from Tableau Public and one chart from Our World in Data (OWID CC BY). For each, answer in a short paragraph: what decision does this figure serve, what comparison does the encoding make easiest, and where does color, axis, or ink either help or quietly mislead. Focus on whether a non-technical reader would leave with a correct takeaway. Use ONLY these URLs.

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