Data science is an iterative process of discovery with well-understood stages.

Question

BlackTom AI · Accepted Answer

Consider the statement being evaluated: 'Data science is an iterative process of discovery with well-understood stages.'
Option 1: True. This aligns with common descriptions of data science as an iterative, cyclical process where discovery leads to questions, experimentation, and refinement. The stages (e.g., problem formulation, data collection, preprocessing, modeling, evaluation, deployment) are often presented as a framework, but in practice they are revisited repeatedly as new insights emerge.
Option 2: False. If we interpret the claim as asserting that the stages are strictly linear and once completed, the process never revisits earlier steps, this is inconsistent with real-world practice. Data science projects typically loop back to redefine questions, gather more data, or adjust models based on results, rather than following a rigid, one-way sequence.
Reframing the core idea, the emphasis on iteration and discovery is central to data science methodologies such as CRISP-DM and other analytics lifecycles, which explicitly incorporate feedback loops and repeated refinement. The idea of 'well-understood stages' is often debated, since while common stages exist, their boundaries can be fuzzy and context-dependent, yet the iterative nature remains a key hallmark.
Therefore, understanding the statement as describing data science as an iterative discovery process, with revisiting stages as needed, supports the True option, while treating the process as strictly linear or non-iterative would support False.

BU.330.780.T1.SP25 Quiz 1- Requires Respondus LockDown Browser

Data science is an iterative process of discovery with well-understood stages.

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