A Q&A addressing practical questions about data science in business contexts: how business requirements are defined and signed off, when to use interpretable vs. black-box models, the role of web scraping in real projects, when to split data engineering and data science roles, and how to know when feature engineering is 'good enough'. Answers draw on real-world experience and emphasize pragmatic, time-boxed approaches.
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