Over the past decade, Big Data have become ubiquitous in all
economic sectors, scientific disciplines, and human activities.
They have led to striking technological advances, affecting all
human experiences. Our ability to manage, understand, interrogate,
and interpret such extremely large, multisource, heterogeneous,
incomplete, multiscale, and incongruent data has not kept pace with
the rapid increase of the volume, complexity and proliferation of
the deluge of digital information. There are three reasons for this
shortfall. First, the volume of data is increasing much faster than
the corresponding rise of our computational processing power
(Kryder's law > Moore's law). Second, traditional
discipline-bounds inhibit expeditious progress. Third, our
education and training activities have fallen behind the
accelerated trend of scientific, information, and communication
advances. There are very few rigorous instructional resources,
interactive learning materials, and dynamic training environments
that support active data science learning. The textbook balances
the mathematical foundations with dexterous demonstrations and
examples of data, tools, modules and workflows that serve as
pillars for the urgently needed bridge to close that supply and
demand predictive analytic skills gap. Exposing the enormous
opportunities presented by the tsunami of Big data, this textbook
aims to identify specific knowledge gaps, educational barriers, and
workforce readiness deficiencies. Specifically, it focuses on the
development of a transdisciplinary curriculum integrating modern
computational methods, advanced data science techniques, innovative
biomedical applications, and impactful health analytics. The
content of this graduate-level textbook fills a substantial gap in
integrating modern engineering concepts, computational algorithms,
mathematical optimization, statistical computing and biomedical
inference. Big data analytic techniques and predictive scientific
methods demand broad transdisciplinary knowledge, appeal to an
extremely wide spectrum of readers/learners, and provide incredible
opportunities for engagement throughout the academy, industry,
regulatory and funding agencies. The two examples below demonstrate
the powerful need for scientific knowledge, computational
abilities, interdisciplinary expertise, and modern technologies
necessary to achieve desired outcomes (improving human health and
optimizing future return on investment). This can only be achieved
by appropriately trained teams of researchers who can develop
robust decision support systems using modern techniques and
effective end-to-end protocols, like the ones described in this
textbook. * A geriatric neurologist is examining a patient
complaining of gait imbalance and posture instability. To determine
if the patient may suffer from Parkinson's disease, the physician
acquires clinical, cognitive, phenotypic, imaging, and genetics
data (Big Data). Most clinics and healthcare centers are not
equipped with skilled data analytic teams that can wrangle,
harmonize and interpret such complex datasets. A learner that
completes a course of study using this textbook will have the
competency and ability to manage the data, generate a protocol for
deriving biomarkers, and provide an actionable decision support
system. The results of this protocol will help the physician
understand the entire patient dataset and assist in making a
holistic evidence-based, data-driven, clinical diagnosis. * To
improve the return on investment for their shareholders, a
healthcare manufacturer needs to forecast the demand for their
product subject to environmental, demographic, economic, and
bio-social sentiment data (Big Data). The organization's
data-analytics team is tasked with developing a protocol that
identifies, aggregates, harmonizes, models and analyzes these
heterogeneous data elements to generate a trend forecast. This
system needs to provide an automated, adaptive, scalable, and
reliable prediction of the optimal investment, e.g., R&D
allocation, that maximizes the company's bottom line. A reader that
complete a course of study using this textbook will be able to
ingest the observed structured and unstructured data,
mathematically represent the data as a computable object, apply
appropriate model-based and model-free prediction techniques. The
results of these techniques may be used to forecast the expected
relation between the company's investment, product supply, general
demand of healthcare (providers and patients), and estimate the
return on initial investments.
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