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Abstract
In today’s information era, where Data galvanizes change, companies are aiming towards competitive advantage by mining this important resource to achieve actionable insights, knowledge, and wisdom. However, to minimize bias and obtain robust long-term solutions, the methodologies that are devised from Data Science and Machine Learning approaches benefit from being carefully validated by a Quality Assurance Data Scientist, who understands not only both business rules and analytics tasks, but also understands and recommends Quality Assurance guidelines and validations.
Through my experience as a Data Scientist at EDP Distribuição, I identify and systematically report on seven key Quality Assurance guidelines that helped achieve more reliable products and provided three practical examples where validation was key in discerning improvements.
