Data science pipelines view data mathematically (e.g., measures, values distribution) first and establish a context for it later relying on computed models that approximate insights and foresights of the phenomena they represent. What a data scientist really would want to be doing is looking at the whole data set in ways that tell her things and answer questions that she is not asking. The pipelines design and its results remain empirical and partially explicit on how statistical tools and computer technologies are used to identify meaningful patterns of information? How shall significant data correlations be interpreted? What is the role…




Genoveva Vargas-Solar

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