國立高雄大學圖資館 |

Data science and predictive analyticsbiomedical and health applications using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science and predictive analyticsby Ivo D. Dinov.
Reminder of title:	biomedical and health applications using R /
Author:	Dinov, Ivo D.
Published:	Cham :Springer International Publishing :2023.
Description:	xxxiv, 918 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Big data.
電子資源:	https://doi.org/10.1007/978-3-031-17483-4
ISBN:	9783031174834$q(electronic bk.)

Data science and predictive analyticsbiomedical and health applications using R /
Dinov, Ivo D.

Data science and predictive analyticsbiomedical and health applications using R /[electronic resource] :by Ivo D. Dinov. - Second edition. - Cham :Springer International Publishing :2023. - xxxiv, 918 p. :ill., digital ;24 cm. - Springer series in applied machine learning,2520-1301. - Springer series in applied machine learning..

Chapter 1 - Introduction -- Chapter 2: Basic Visualization and Exploratory Data Analytics -- Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling -- Chapter 4: Linear and Nonlinear Dimensionality Reduction -- Chapter 5: Supervised Classification -- Chapter 6: Black Box Machine Learning Methods -- Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning -- Chapter 8: Unsupervised Clustering -- Chapter 9: Model Performance Assessment, Validation, and Improvement -- Chapter 10: Specialized Machine Learning Topics -- Chapter 11: Variable Importance and Feature Selection -- Chapter 12: Big Longitudinal Data Analysis -- Chapter 13: Function Optimization -- Chapter 14: Deep Learning, Neural Networks.

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 this textbook address specific knowledge gaps, resolve educational barriers, and mitigate workforce information readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical foundations, modern computational methods, advanced data science techniques, model-based machine learning (ML), model-free artificial intelligence (AI), and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build the 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. Individual modules and complete end-to-end pipeline protocols are available as functional R electronic markdown notebooks. These workflows support an active learning platform for comprehensive data manipulation, sophisticated analytics, interactive visualization, and effective dissemination of open problems, current knowledge, scientific tools, and research findings. This Second Edition includes new material reflecting recent scientific and technological progress and a substantial content reorganization to streamline the covered topics. Featured are learning-based strategies utilizing generative adversarial networks (GANs), transfer learning, and synthetic data generation. There are complete end-to-end examples of ML/AI training, prediction, and assessment using quantitative, qualitative, text, and imaging datasets. This textbook is suitable for self-learning and instructor-guided course training. It is appropriate for upper division and graduate-level courses covering 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 spectrum of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory and funding agencies.

ISBN: 9783031174834$q(electronic bk.)

Standard No.: 10.1007/978-3-031-17483-4doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: Q336 / .D56 2023

Dewey Class. No.: 005.7

Data science and predictive analyticsbiomedical and health applications using R /
LDR:04515nmm a2200349 a 4500 001 639761
003 DE-He213
005 20230216073924.0
006 m d
007 cr nn 008maaau
008 231025s2023 sz s 0 eng d
020 $a 9783031174834$q(electronic bk.)
020 $a 9783031174827$q(paper)
024 7 $a 10.1007/978-3-031-17483-4 $2 doi
035 $a 978-3-031-17483-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q336 $b .D56 2023
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
090 $a Q336 $b .D586 2023
100 1 $a Dinov, Ivo D. $3 821965
245 1 0 $a Data science and predictive analytics $h [electronic resource] : $b biomedical and health applications using R / $c by Ivo D. Dinov.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxxiv, 918 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in applied machine learning, $x 2520-1301
505 0 $a Chapter 1 - Introduction -- Chapter 2: Basic Visualization and Exploratory Data Analytics -- Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling -- Chapter 4: Linear and Nonlinear Dimensionality Reduction -- Chapter 5: Supervised Classification -- Chapter 6: Black Box Machine Learning Methods -- Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning -- Chapter 8: Unsupervised Clustering -- Chapter 9: Model Performance Assessment, Validation, and Improvement -- Chapter 10: Specialized Machine Learning Topics -- Chapter 11: Variable Importance and Feature Selection -- Chapter 12: Big Longitudinal Data Analysis -- Chapter 13: Function Optimization -- Chapter 14: Deep Learning, Neural Networks.
520 $a 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 this textbook address specific knowledge gaps, resolve educational barriers, and mitigate workforce information readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical foundations, modern computational methods, advanced data science techniques, model-based machine learning (ML), model-free artificial intelligence (AI), and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build the 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. Individual modules and complete end-to-end pipeline protocols are available as functional R electronic markdown notebooks. These workflows support an active learning platform for comprehensive data manipulation, sophisticated analytics, interactive visualization, and effective dissemination of open problems, current knowledge, scientific tools, and research findings. This Second Edition includes new material reflecting recent scientific and technological progress and a substantial content reorganization to streamline the covered topics. Featured are learning-based strategies utilizing generative adversarial networks (GANs), transfer learning, and synthetic data generation. There are complete end-to-end examples of ML/AI training, prediction, and assessment using quantitative, qualitative, text, and imaging datasets. This textbook is suitable for self-learning and instructor-guided course training. It is appropriate for upper division and graduate-level courses covering 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 spectrum of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory and funding agencies.
650 0 $a Big data. $3 609582
650 1 4 $a Data Science. $3 913495
650 2 4 $a Data Analysis and Big Data. $3 913147
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Health Informatics. $3 274212
650 2 4 $a Big Data. $3 760530
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer series in applied machine learning. $3 946141
856 4 0 $u https://doi.org/10.1007/978-3-031-17483-4
950 $a Computer Science (SpringerNature-11645)