Data Science and Predictive Analytics (Record no. 14983)

MARC details
000 -LEADER
fixed length control field 03881nam a2200373Ia 4500
000 - LEADER
fixed length control field 04991nam a22004095i 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 20240319120803.0
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
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 5.7
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Dinov, Ivo D.
9 (RLIN) 28731
245 ## - TITLE STATEMENT
Title Data Science and Predictive Analytics
Statement of responsibility, etc. by Ivo D. Dinov.
Medium [electronic resource] :
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2023.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cham
Name of publisher, distributor, etc. Springer International Publishing
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent XXXIV, 918 p. 336 illus., 306 illus. in color.
Other physical details online resource.
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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence
9 (RLIN) 28732
Topical term or geographic name entry element Big data.
9 (RLIN) 28733
Topical term or geographic name entry element Big Data.
9 (RLIN) 28734
Topical term or geographic name entry element Data Analysis and Big Data.
9 (RLIN) 28735
Topical term or geographic name entry element Data Mining and Knowledge Discovery.
9 (RLIN) 28736
Topical term or geographic name entry element Data mining.
9 (RLIN) 28737
Topical term or geographic name entry element Data Science.
9 (RLIN) 28738
Topical term or geographic name entry element Health Informatics.
9 (RLIN) 28739
Topical term or geographic name entry element Machine learning.
9 (RLIN) 28740
Topical term or geographic name entry element Machine Learning.
9 (RLIN) 28741
Topical term or geographic name entry element Medical informatics.
9 (RLIN) 28742
Topical term or geographic name entry element Quantitative research.
9 (RLIN) 28743
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-17483-4">https://doi.org/10.1007/978-3-031-17483-4</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type e-Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type Public note
        S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Online   Veda Library Solutions Pvt. Ltd., Noida   EB1969 2024-03-19 2024-03-19 e-Book Platform:Springer