Data Types, Measurement & Variables
Data Types, Measurement & Variables groups the core ideas a learner needs at the foundations stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
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
Numeric, categorical and boolean variablesNumeric, categorical and boolean variables is a practical concept within Data Types, Measurement & Variables. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Nominal, ordinal, interval and ratio scalesNominal, ordinal, interval and ratio scales changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
03Discrete vs continuous variablesDiscrete vs continuous variables is a practical concept within Data Types, Measurement & Variables. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Identifiers, keys and metadataIdentifiers, keys and metadata is a core data-integration operation. In analytics, correctness depends not only on syntax but on the relationship between keys, row cardinality and the grain of each table.
05Features, targets and labelsFeatures, targets and labels changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
06Wide vs long dataWide vs long data is a practical concept within Data Types, Measurement & Variables. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.