Data Understanding & Exploratory Analysis
Inspect structure, distributions, relationships, missingness and anomalies before choosing transformations or models. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Schema and data typesA schema describes variables, types, units, allowed ranges, keys and relationships. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Descriptive statisticsDescriptive summaries quantify centre, spread, frequency and shape. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Distribution visualisationHistograms, density plots, boxplots and empirical CDFs reveal shape, tails and outliers. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Relationships and correlationPairwise plots, correlations and grouped summaries explore associations among variables. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Missingness profilingMissing data patterns can contain information about collection processes and bias. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.