Overview
- Transdisciplinary treatment integrates novel computational methods, statistical inference techniques, data science tools
- Includes many hands-on demonstrations using imaging, environmental, health and clinical case-studies
- Promotes open-source code, data sharing, and open-science principles
Part of the book series: The Springer Series in Applied Machine Learning (SSAML)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
- big data
- R
- statistical computing
- predictive analytics
- data science
- health analytics
- machine learning
- statistical learning in R
- hands-on machine learning
- Big Data methods
- data management
- streaming
- visualization
- neural networks
- controlled variable selection
- text mining
- natural language processing
- cross-validation
- deep learning
About this book
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 Analytics 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 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 notebooks, extensive appendices, and additional materials.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Data Science and Predictive Analytics
Book Subtitle: Biomedical and Health Applications using R
Authors: Ivo D. Dinov
Series Title: The Springer Series in Applied Machine Learning
DOI: https://doi.org/10.1007/978-3-031-17483-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-17482-7Published: 17 February 2023
Softcover ISBN: 978-3-031-17485-8Published: 17 February 2024
eBook ISBN: 978-3-031-17483-4Published: 16 February 2023
Series ISSN: 2520-1298
Series E-ISSN: 2520-1301
Edition Number: 2
Number of Pages: XXXIV, 918
Number of Illustrations: 30 b/w illustrations, 306 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Statistics, general, Machine Learning, Health Informatics, Big Data