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Books > Computing & IT > Applications of computing > Artificial intelligence
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Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data (Hardcover, Edition.)
Loot Price: R1,898
Discovery Miles 18 980
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Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data (Hardcover, Edition.)
Series: Texts in Computer Science
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Each passing year bears witness to the development of ever more
powerful computers, increasingly fast and cheap storage media, and
even higher bandwidth data connections. This makes it easy to
believe that we can now - at least in principle - solve any problem
we are faced with so long as we only have enough data. Yet this is
not the case. Although large databases allow us to retrieve many
different single pieces of information and to compute simple
aggregations, general patterns and regularities often go
undetected. Furthermore, it is exactly these patterns, regularities
and trends that are often most valuable. To avoid the danger of
"drowning in information, but starving for knowledge" the branch of
research known as data analysis has emerged, and a considerable
number of methods and software tools have been developed. However,
it is not these tools alone but the intelligent application of
human intuition in combination with computational power, of sound
background knowledge with computer-aided modeling, and of critical
reflection with convenient automatic model construction, that
results in successful intelligent data analysis projects. Guide to
Intelligent Data Analysis provides a hands-on instructional
approach to many basic data analysis techniques, and explains how
these are used to solve data analysis problems. Topics and
features: guides the reader through the process of data analysis,
following the interdependent steps of project understanding, data
understanding, data preparation, modeling, and deployment and
monitoring; equips the reader with the necessary information in
order to obtain hands-on experience of the topics under discussion;
provides a review of the basics of classical statistics that
support and justify many data analysis methods, and a glossary of
statistical terms; includes numerous examples using R and KNIME,
together with appendices introducing the open source software;
integrates illustrations and case-study-style examples to support
pedagogical exposition. This practical and systematic
textbook/reference for graduate and advanced undergraduate students
is also essential reading for all professionals who face data
analysis problems. Moreover, it is a book to be used following
one's exploration of it. Dr. Michael R. Berthold is
Nycomed-Professor of Bioinformatics and Information Mining at the
University of Konstanz, Germany. Dr. Christian Borgelt is Principal
Researcher at the Intelligent Data Analysis and Graphical Models
Research Unit of the European Centre for Soft Computing, Spain. Dr.
Frank Hoeppner is Professor of Information Systems at Ostfalia
University of Applied Sciences, Germany. Dr. Frank Klawonn is a
Professor in the Department of Computer Science and Head of the
Data Analysis and Pattern Recognition Laboratory at Ostfalia
University of Applied Sciences, Germany. He is also Head of the
Bioinformatics and Statistics group at the Helmholtz Centre for
Infection Research, Braunschweig, Germany.
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