Conceptual Frameworks for Data Science Projects

Conceptual frameworks serve as analytical structures for data scientists to organize abstract concepts, plan projects, and select models. Four common types are particularly useful in data science: hierarchies, matrices, process flows, and relational maps. Hierarchies use tree-like diagrams to break down concepts, while matrices compare different dimensions in a tabular format to reveal insights. Process flows outline a sequence of activities to achieve a goal, and relational maps focus on the connections between entities, such as causal or similarity-based relationships. Understanding these frameworks helps in structuring problems and communicating findings effectively.