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This book provides a practical and fairly comprehensive review of
Data Science through the lens of dimensionality reduction, as well
as hands-on techniques to tackle problems with data collected in
the real world. State-of-the-art results and solutions from
statistics, computer science and mathematics are explained from the
point of view of a practitioner in any domain science, such as
biology, cyber security, chemistry, sports science and many others.
Quantitative and qualitative assessment methods are described to
implement and validate the solutions back in the real world where
the problems originated. The ability to generate, gather and store
volumes of data in the order of tera- and exo bytes daily has far
outpaced our ability to derive useful information with available
computational resources for many domains. This book focuses on data
science and problem definition, data cleansing, feature selection
and extraction, statistical, geometric, information-theoretic,
biomolecular and machine learning methods for dimensionality
reduction of big datasets and problem solving, as well as a
comparative assessment of solutions in a real-world setting. This
book targets professionals working within related fields with an
undergraduate degree in any science area, particularly
quantitative. Readers should be able to follow examples in this
book that introduce each method or technique. These motivating
examples are followed by precise definitions of the technical
concepts required and presentation of the results in general
situations. These concepts require a degree of abstraction that can
be followed by re-interpreting concepts like in the original
example(s). Finally, each section closes with solutions to the
original problem(s) afforded by these techniques, perhaps in
various ways to compare and contrast dis/advantages to other
solutions.
Locality is a fundamental restriction in nature. On the other hand,
adaptive complex systems, life in particular, exhibit a sense of
permanence and time lessness amidst relentless constant changes in
surrounding environments that make the global properties of the
physical world the most important problems in understanding their
nature and structure. Thus, much of the differential and integral
Calculus deals with the problem of passing from local information
(as expressed, for example, by a differential equation, or the
contour of a region) to global features of a system's behavior (an
equation of growth, or an area). Fundamental laws in the exact
sciences seek to express the observable global behavior of physical
objects through equations about local interaction of their
components, on the assumption that the continuum is the most
accurate model of physical reality. Paradoxically, much of modern
physics calls for a fundamen tal discrete component in our
understanding of the physical world. Useful computational models
must be eventually constructed in hardware, and as such can only be
based on local interaction of simple processing elements."
This book provides a practical and fairly comprehensive review of
Data Science through the lens of dimensionality reduction, as
well as hands-on techniques to tackle problems with data collected
in the real world. State-of-the-art results and solutions from
statistics, computer science and mathematics are explained from the
point of view of a practitioner in any domain science, such as
biology, cyber security, chemistry, sports science and many others.
Quantitative and qualitative assessment methods are described to
implement and validate the solutions back in the real world where
the problems originated. The ability to generate, gather and store
volumes of data in the order of tera- and exo bytes daily has far
outpaced our ability to derive useful information with available
computational resources for many domains. This book focuses on data
science and problem definition, data cleansing, feature selection
and extraction, statistical, geometric, information-theoretic,
biomolecular and machine learning methods for dimensionality
reduction of big datasets and problem solving, as well as a
comparative assessment of solutions in a real-world setting. This
book targets professionals working within related fields with an
undergraduate degree in any science area, particularly
quantitative. Readers should be able to follow examples in this
book that introduce each method or technique. These motivating
examples are followed by precise definitions of the technical
concepts required and presentation of the results in general
situations. These concepts require a degree of abstraction that can
be followed by re-interpreting concepts like in the original
example(s). Finally, each section closes with solutions to the
original problem(s) afforded by these techniques, perhaps in
various ways to compare and contrast dis/advantages to other
solutions.
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