Titlebook: Mechanistic Data Science for STEM Education and Applications; Wing Kam Liu,Zhengtao Gan,Mark Fleming Textbook 2021 The Editor(s) (if appli

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arbage collection feasible in many situations, including real time applications or within traditional programming languages. However optimal performance cannot always be achieved by a uniform general purpose solution. Sometimes an algorithm exhibits a predictable pattern of memory usage that could b

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problems by combining data science and mechanistic knowledg.This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems.  Traditional data scie

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Multimodal Data Generation and Collection,making aspects. This chapter shows data collection and generation from different sources and how they can be managed efficiently. Feature-based diamond pricing and material property testing by indentation are used to demonstrate key ideas.

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Optimization and Regression, some nonlinear relationships will also be discussed, including piecewise linear regression, and moving least squares. The ease and strength of linear regression will be demonstrated through example problems in baseball and material hardness.

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Textbook 2021les (i.e., “mechanistic” principles) to solve intractable problems.  Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here

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engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM. .(Science, Technology, Engineering, Mathematics). .high school students and teachers..978-3-030-87834-4978-3-030-87832-0