Data ComprehensionBasic comprehension of data related terms and conventions of data visualization. For example, knowing the difference between a median and an average or a visualization that is at log or linear scale.
Cause & EffectKnowledge related to cause and effect such as the idea that correlation doesn't equal causation.
Fallacies & BiasesKnowledge of common fallacies related to data such as the prosecutors fallacy.
Scientific MethodKnowledge of the scientific method such as experiment controls. This is required to properly analyze data or perform research. For example, the principle that you must have a hypothesis first before running an experiment as opposed to fitting your hypothesis to the data afterwards.
NuanceAn ability to investigate and model the nuances of data. For example, a survey question framed two different ways may lead to completely different results.
Data AnalysisData analysis is the practice of interpreting data. For example, the process of identifying and resolving data quality issues.
Statistical AnalysisStatistics is the mathematics of data collection, organization, analysis, interpretation and presentation. For example, the ability to develop and validate a statistical model that can be used to forecast monthly sales for a product.
Data VisualizationThe ability to visual data in a way that accurately communicates meaning without being misleading.
ContextThe ability to analyze and communicate the context of data. For example, a marketing person investigating the sales impact of different promotional campaigns who considers context such as the prices or holidays that also influence sales.
Data CommunicationThe ability to tell a story with data without being misleading.
Data ToolsThe ability to use data related applications productively in areas such as analytics, business intelligence and database query languages.
|Overview: Data Literacy|
The ability to understand, interpret, critique, analyze and communicate data.