Data-Science Lifecycle

The data-science lifecycle is the end-to-end sequence a project moves through: frame a question, collect and clean the data, explore and analyse it, model if needed, and finally communicate results so someone can act. It is usually drawn as a loop because findings often send you back to refine the question or gather better data.

Treating it as a checklist keeps a project honest: skipping the framing step produces precise answers to the wrong question, while skipping cleaning lets garbage flow straight into the model. Real projects rarely flow neatly forward; the value of the lifecycle is reminding you that analysis is only one stage, sandwiched between asking well and deciding well.