1. Descriptive Analytics
Diagnose what happened using distributions, defensible metrics, cohorts and visual evidence
1. Descriptive Analytics
Descriptive analytics reconstructs what happened to a defined population. It does not merely “summarise the data”: it tests whether an aggregate hides skew, timing, selection or operational failure.
Learning outcomes
After this module, you can:
- compare centre, spread, tail and subgroup distributions;
- define a KPI with numerator, denominator, unit, period and exclusions;
- build cohort and funnel tables without changing populations mid-analysis;
- select a visual encoding for the reader’s comparison task;
- separate observational patterns from experimental causal evidence.
Chapter route
| Chapter | Diagnostic question | Output |
|---|---|---|
| Distributions and EDA | Is the average hiding variation or data failure? | distribution profile |
| KPIs and Denominators | Exactly what entered the numerator and denominator? | metric contract and driver tree |
| Segments, Cohorts and Funnels | Which comparable groups follow different paths? | cohort/funnel diagnosis |
| Visual Evidence | What comparison must the reader make? | decision display |
| Experiments and Causal Boundaries | Did an action cause the observed difference? | experiment or limitation statement |
Prepare, work, follow up
- Prepare: bring one dashboard metric and write its denominator from memory.
- Workshop: reproduce it from a six-row table, then find two ways the definition could change the result.
- Follow up: replace one headline average with a distribution, comparison group and uncertainty statement.
The module uses tables as text alternatives to every visual argument. Students may submit a written chart specification when drawing software is inaccessible.