Data Science and Predictive Analytics (Registro nro. 260801)

MARC details
000 -LIDER
fixed length control field 05772nam a22006375i 4500
001 - CONTROL NUMBER
control field 978-3-031-17483-4
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240207153511.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230216s2023 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031174834
-- 978-3-031-17483-4
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q336
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM031000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Dinov, Ivo D.
Relator term author.
Authority record control number (orcid)0000-0003-3825-4375
-- https://orcid.org/0000-0003-3825-4375
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Data Science and Predictive Analytics
Medium [electronic resource] :
Remainder of title Biomedical and Health Applications using R /
Statement of responsibility, etc. by Ivo D. Dinov.
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2023.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2023.
300 ## - PHYSICAL DESCRIPTION
Extent XXXIV, 918 p. 336 illus., 306 illus. in color.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement The Springer Series in Applied Machine Learning,
International Standard Serial Number 2520-1301
500 ## - GENERAL NOTE
General note Acceso multiusuario
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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 ## - SUMMARY, ETC.
Summary, etc. 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.
541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE
Owner UABC ;
Method of acquisition Perpetuidad
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Artificial intelligence
Subdivisión general Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Quantitative research.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Medical informatics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Big data.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data mining.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data Analysis and Big Data.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Health Informatics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Big Data.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data Mining and Knowledge Discovery.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031174827
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031174841
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031174858
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title The Springer Series in Applied Machine Learning,
-- 2520-1301
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Libro electrónico
Uniform Resource Identifier http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-17483-4
912 ## -
-- ZDB-2-SCS
912 ## -
-- ZDB-2-SXCS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Libro Electrónico
Existencias
Estado de retiro Colección Ubicación permanente Ubicación actual Fecha de ingreso Total Checkouts Date last seen Número de copia Tipo de material
  Colección de Libros Electrónicos Biblioteca Electrónica Biblioteca Electrónica 07/02/2024   07/02/2024 1 Libro Electrónico

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