Abstract
The Big data challenge includes dealing with a big number of, heterogeneous and multidimensional, datasets, of all possible sizes, not only with the data of big size. As a result, a huge number of Machine Learning (ML) tasks, which must be solved, dramatically exceeds the number of the data scientists, who can solve these tasks. Next, many ML tasks require the critical input, from the subject matter experts (SME), and end users/decision makers, who are not ML experts. A set of tools, which we call a “virtual data scientist” is needed to assist the SMEs, and end users to construct the ML models, for their tasks, to meet this Big data challenge, with a minimal contribution from data scientists. This chapter describes our vision of such a “virtual data scientist”, based on the visual approach of the General Line Coordinates.
All generalizations are false, including this one.
Mark Twain
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Kovalerchuk, B. (2018). Toward Virtual Data Scientist and Super-Intelligence with Visual Means. In: Visual Knowledge Discovery and Machine Learning. Intelligent Systems Reference Library, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-73040-0_12
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DOI: https://doi.org/10.1007/978-3-319-73040-0_12
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