An interactive textbook
Data Driven Decision Making
From Business Questions to Visual Evidence, Algorithms, and AI Workflows
An expandable online book that moves from raw business questions to visual evidence, causal estimates, machine-learning models, and modern AI workflows — each chapter paired with real datasets and hands-on interactive studios.
Treat data as a trace of business activity, then route the question through storage, evidence, and a decision someone can own.
Before you ask what model to run, ask what one row means, what shape the table is in, and what kind of column you are looking at.
Spreadsheet operations, written down as reproducible code — and the conceptual errors that hide inside correct-looking queries.
Pick the chart from the question, not the question from the chart — and the definition underneath the metric matters more than the metric.
- 3.1Exploratory Visualization and Dashboards
- 3.2Chart Atlas
- 3.3Case Study: Market Concentration Metrics
Every chart is a comparison — name the baseline, show the spread, and end on a decision, not a pattern.
Every metric worth acting on hides a counterfactual you must construct, not assume.
A regression number is only as trustworthy as the comparison it secretly makes.
Build the missing counterfactual — then trust the effect only as far as the design that produced it.
Turning an identified elasticity into a defensible price, then into a memo a committee can act on.
Get the task contract and the features right, and the algorithm almost picks itself; get them wrong, and no model can save you.
A model isn't evaluated until its scores meet the firm's cost matrix and ship with a card.
Unsupervised methods don't hand you answers — they hand you a lens, and a manager decides whether the structure is worth acting on.
Turning scores into audiences, rankings, and a monitored system that still works six months later.
Turning reviews, tickets, and transcripts into evidence a model can act on — and knowing exactly where word counts stop working.
From counting words, to placing meaning in coordinates, to measuring the constructs a manager actually cares about.
One shared embedding space, four production patterns: ground the text, see the image, read the document, reason across all of them.
An LLM is a language interface for workflows — value lives in the wiring, the gates, and the governance, not the model.
Turning a stack of analyses into an operating system that compounds instead of decays.