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Books > Computing & IT > Applications of computing > Artificial intelligence
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Visual Analytics for Data Scientists (Paperback, 1st ed. 2020)
Loot Price: R1,939
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Visual Analytics for Data Scientists (Paperback, 1st ed. 2020)
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This textbook presents the main principles of visual analytics and
describes techniques and approaches that have proven their utility
and can be readily reproduced. Special emphasis is placed on
various instructive examples of analyses, in which the need for and
the use of visualisations are explained in detail. The book begins
by introducing the main ideas and concepts of visual analytics and
explaining why it should be considered an essential part of data
science methodology and practices. It then describes the general
principles underlying the visual analytics approaches, including
those on appropriate visual representation, the use of interactive
techniques, and classes of computational methods. It continues with
discussing how to use visualisations for getting aware of data
properties that need to be taken into account and for detecting
possible data quality issues that may impair the analysis. The
second part of the book describes visual analytics methods and
workflows, organised by various data types including
multidimensional data, data with spatial and temporal components,
data describing binary relationships, texts, images and video. For
each data type, the specific properties and issues are explained,
the relevant analysis tasks are discussed, and appropriate methods
and procedures are introduced. The focus here is not on the
micro-level details of how the methods work, but on how the methods
can be used and how they can be applied to data. The limitations of
the methods are also discussed and possible pitfalls are
identified. The textbook is intended for students in data science
and, more generally, anyone doing or planning to do practical data
analysis. It includes numerous examples demonstrating how visual
analytics techniques are used and how they can help analysts to
understand the properties of data, gain insights into the subject
reflected in the data, and build good models that can be trusted.
Based on several years of teaching related courses at the City,
University of London, the University of Bonn and TU Munich, as well
as industry training at the Fraunhofer Institute IAIS and numerous
summer schools, the main content is complemented by sample datasets
and detailed, illustrated descriptions of exercises to practice
applying visual analytics methods and workflows.
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