A comprehensive introduction to statistical methods for data mining
and knowledge discovery. Applications of data mining and big data
increasingly take center stage in our modern, knowledge-driven
society, supported by advances in computing power, automated data
acquisition, social media development and interactive, linkable
internet software. This book presents a coherent, technical
introduction to modern statistical learning and analytics, starting
from the core foundations of statistics and probability. It
includes an overview of probability and statistical distributions,
basics of data manipulation and visualization, and the central
components of standard statistical inferences. The majority of the
text extends beyond these introductory topics, however, to
supervised learning in linear regression, generalized linear
models, and classification analytics. Finally, unsupervised
learning via dimension reduction, cluster analysis, and market
basket analysis are introduced. Extensive examples using actual
data (with sample R programming code) are provided, illustrating
diverse informatic sources in genomics, biomedicine, ecological
remote sensing, astronomy, socioeconomics, marketing, advertising
and finance, among many others. Statistical Data Analytics: *
Focuses on methods critically used in data mining and statistical
informatics. Coherently describes the methods at an introductory
level, with extensions to selected intermediate and advanced
techniques. * Provides informative, technical details for the
highlighted methods. * Employs the open-source R language as the
computational vehicle along with its burgeoning collection of
online packages to illustrate many of the analyses contained in the
book. * Concludes each chapter with a range of interesting and
challenging homework exercises using actual data from a variety of
informatic application areas. This book will appeal as a classroom
or training text to intermediate and advanced undergraduates, and
to beginning graduate students, with sufficient background in
calculus and matrix algebra. It will also serve as a source-book on
the foundations of statistical informatics and data analytics to
practitioners who regularly apply statistical learning to their
modern data.
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