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Agile Data Science is about solving problems, not models or algorithms.
All validation of data, hypotheses and performance should be tracked, reviewed and automated.
Prior to building a model, construct an evaluation framework with end-to-end business focused acceptance criteria.
A product needs a pool of measures to evaluate its quality. A single number cannot capture the complexity of reality.
Even research can be broken down into clearly defined tasks. The smallest of iterations should be preferred in acquiring, integrating and correcting knowledge.
Don’t neglect assumptions in models. Make them explicit then aim to have them either verified or removed.