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Abstract:
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Data Science is ubiquitous in the era where "we measure everything." It has touched virtually every academic discipline, government, and industry. Despite these developments and decades of research, several open problems remain. One such questions is: what are the fundamental building blocks of statistical learning that allow for a systematic and unified approach towards data modeling (the foundation of data science)? Can we hope that such a Theory of Data Science (ToDS) exists? Despite past skepticism, recently there has been substantial progress (Mukhopadhyay and Parzen, 2014a, Parzen and Mukhopadhyay, 2013c,b,a)) that strongly suggests the existence of such a theory.
In this lecture, I will introduce this new frontier of research.
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