Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical

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ainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi978-3-030-10187-9978-3-319-72347-1

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Linear Algebra & Matrix Computing,is generally challenging to visualize complex data, e.g., large vectors, tensors, and tables in n-dimensional Euclidian spaces (.?≥?3). Linear algebra allows us to mathematically represent, computationally model, statistically analyze, synthetically simulate, and visually summarize such complex data.

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Dimensionality Reduction,ber of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variables as functions of the old features.

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