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Data Science and Predictive Analytics: Biomedical and Health Applications using R

By: Dinov, Ivo D
Publisher: Suiza: Springer, 2018Description: 851 p. DigitalSubject(s): DATA SCIENCE | ANALYTICS | TECNOLOGY | ENGINEERING | MATHEMATICAS | BIOMEDICAL | IIC20DDC classification: Online resources: ►►DOWNLOAD EBOOK / DESCARGAR LIBRO DIGITAL◄◄
Contents:
REFERENCIA FORMATO APA: Dinov, I. (2018). Data science and predictive analytics : biomedical and health applications using R. Cham, Switzerland: Springer.
Summary: The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook.
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REFERENCIA FORMATO APA: Dinov, I. (2018). Data science and predictive analytics : biomedical and health applications using R. Cham, Switzerland: Springer.

The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies.

The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook.

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