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Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
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Applied Data Analytics - Principles and Applications (Hardcover)
Loot Price: R2,971
Discovery Miles 29 710
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Applied Data Analytics - Principles and Applications (Hardcover)
Series: River Publishers Series in Signal, Image and Speech Processing
Expected to ship within 12 - 17 working days
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The emergence of huge amounts of data which require analysis and in
some cases real-time processing has forced exploration into fast
algorithms for handling very lage data sizes. Analysis of x-ray
images in medical applications, cyber security data, crime data,
telecommunications and stock market data, health records and
business analytics data are but a few areas of interest.
Applications and platforms including R, RapidMiner and Weka provide
the basis for analysis, often used by practitioners who pay little
to no attention to the underlying mathematics and processes
impacting the data. This often leads to an inability to explain
results or correct mistakes, or to spot errors. Applied Data
Analytics - Principles and Applications seeks to bridge this
missing gap by providing some of the most sought after techniques
in big data analytics. Establishing strong foundations in these
topics provides practical ease when big data analyses are
undertaken using the widely available open source and commercially
orientated computation platforms, languages and visualisation
systems. The book, when combined with such platforms, provides a
complete set of tools required to handle big data and can lead to
fast implementations and applications. The book contains a mixture
of machine learning foundations, deep learning, artificial
intelligence, statistics and evolutionary learning mathematics
written from the usage point of view with rich explanations on what
the concepts mean. The author has thus avoided the complexities
often associated with these concepts when found in research papers.
The tutorial nature of the book and the applications provided are
some of the reasons why the book is suitable for undergraduate,
postgraduate and big data analytics enthusiasts. This text should
ease the fear of mathematics often associated with practical data
analytics and support rapid applications in artificial
intelligence, environmental sensor data modelling and analysis,
health informatics, business data analytics, data from Internet of
Things and deep learning applications.
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