This textbook integrates important mathematical foundations,
efficient computational algorithms, applied statistical inference
techniques, and cutting-edge machine learning approaches to address
a wide range of crucial biomedical informatics, health analytics
applications, and decision science challenges. Each concept in the
book includes a rigorous symbolic formulation coupled with
computational algorithms and complete end-to-end pipeline protocols
implemented as functional R electronic markdown notebooks. These
workflows support active learning and demonstrate comprehensive
data manipulations, interactive visualizations, and sophisticated
analytics. The content includes open problems, state-of-the-art
scientific knowledge, ethical integration of heterogeneous
scientific tools, and procedures for systematic validation and
dissemination of reproducible research findings.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.
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