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 |
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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 |