DATA SCIENCE AND PREDICTIVE ANALYTICS: BIOMEDICAL AND HEALTH APPLICATIONS USING R (THE SPRINGER SERIES IN APPLIED MACHINE LEARNING)
DINOV, IVO D.
ISBN 10: 3031174852 ISBN 13: 9783031174858
Editorial: Springer, 2024
Nuevos
Encuadernación de tapa blanda
Librería:
Basi6 International, Irving, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Vendedor de AbeBooks desde 24 de junio de 2016
Este artículo en concreto ya no está disponible.
Descripción
Descripción:
New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N° de ref. del artículo ABEJUNE24-376525
Sinopsis:
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges.
Acerca del autor: Professor Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR) at the University of Michigan and serves as associate director of the Michigan Institute for Data Science (MIDAS). He is an expert in mathematical modeling, statistical analysis, high-throughput computational processing, and scientific visualization of large, complex and heterogeneous datasets (Big Data). Dr. Dinov is developing, validating, and disseminating novel technology-enhanced pedagogical approaches for STEM education and active data science learning. His artificial intelligence and machine learning work involves compressive big data analytics, statistical obfuscation of sensitive data, complex time (kime) representation, model-based and model-free techniques for kimesurface analytics. Dr. Dinov is a member of the American Statistical Association, the American Mathematical Society, the American Physical Society, the American Association for the Advancement of Science, an honorary member ofthe Sigma Theta Tau International Society, and an elected member of the International Statistical Institute.
"Sobre este título" puede pertenecer a otra edición de este libro.
Detalles bibliográficos
Título: DATA SCIENCE AND PREDICTIVE ANALYTICS: ...
Editorial: Springer
Año de publicación: 2024
Encuadernación: Encuadernación de tapa blanda
Condición: Brand New
Edición: 2ª Edición
Los mejores resultados en AbeBooks
Imagen del vendedor
Data Science and Predictive Analytics
Publicado por
Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031174852
ISBN 13: 9783031174858
Nuevo
Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analyti. Nº de ref. del artículo: 1362013022
Gastos de envío:
EUR 48,99
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Data Science and Predictive Analytics
Publicado por
Springer International Publishing, Springer International Publishing Feb 2024, 2024
ISBN 10: 3031174852
ISBN 13: 9783031174858
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown not Elektronisches Buch. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 952 pp. Englisch. Nº de ref. del artículo: 9783031174858
Gastos de envío:
EUR 60,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen del vendedor
Data Science and Predictive Analytics : Biomedical and Health Applications using R
Publicado por
Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031174852
ISBN 13: 9783031174858
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown not Elektronisches Buch. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analyticsaddress specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices.This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic not Elektronisches Buch, extensive appendices, and additional materials. Nº de ref. del artículo: 9783031174858
Gastos de envío:
EUR 67,05
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen del vendedor
Data Science and Predictive Analytics
Publicado por
Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031174852
ISBN 13: 9783031174858
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown not Elektronisches Buch. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analyticsaddress specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices.This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic not Elektronisches Buch, extensive appendices, and additional materials. 918 pp. Englisch. Nº de ref. del artículo: 9783031174858
Gastos de envío:
EUR 23,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen de archivo
Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 2nd ed. 2023 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26398895919
Cantidad disponible: 1 disponibles
Imagen de archivo
Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)
Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 397481200
Gastos de envío:
EUR 7,49
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen de archivo
Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18398895909
Gastos de envío:
EUR 9,95
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles