Mathar Rudolf (inter alios) - Fundamentals Of Data Analytics, With A View To Machine Learning.pdf

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Rudolf Mathar
Gholamreza Alirezaei
Emilio Balda
Arash Behboodi
Fundamentals
of Data
Analytics
With a View to Machine Learning
Fundamentals of Data Analytics
Rudolf Mathar Gholamreza Alirezaei
Emilio Balda Arash Behboodi
•
•
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Fundamentals of Data
Analytics
With a View to Machine Learning
123
Rudolf Mathar
Institute for Theoretical Information
Technology
RWTH Aachen University
Aachen, Nordrhein-Westfalen, Germany
Emilio Balda
Institute for Theoretical Information
Technology
RWTH Aachen University
Aachen, Nordrhein-Westfalen, Germany
Gholamreza Alirezaei
Chair and Institute for Communications
Engineering
RWTH Aachen University
Aachen, Nordrhein-Westfalen, Germany
Arash Behboodi
Institute for Theoretical Information
Technology
RWTH Aachen University
Aachen, Nordrhein-Westfalen, Germany
ISBN 978-3-030-56830-6
ISBN 978-3-030-56831-3
https://doi.org/10.1007/978-3-030-56831-3
(eBook)
©
Springer Nature Switzerland AG 2020
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Preface
Data Analytics is a fast developing interdisciplinary branch of science combining
methods from exploratory statistics, algorithm and information theory to reveal
structures in large data sets. Systematic patterns are often concealed by the high
dimension and the sheer mass of the data. Diagram 1 visualizes which skills are
important for successful data science. Computer Science, Statistics and substantive
expertise in the respective application
field
contribute to the
field
of Data Science.
Moreover, Machine Learning, statistical analysis and tailored applications all rely
on methods from data analytics. To be a successful researcher in data science, one
should be experienced in and open to methods from each of the domains.
The difference to classical approaches about 20 years ago mainly lies in the
tremendous size of the data that can be handled and the huge dimension of
observations. This was previously not accessible, but by the increasing efficiency
and speed of computers and the development of parallel and distributed algorithms
problems of unanticipated size can be solved at present.
Data analytics is a crucial tool for internet search, marketing, medical image
analysis, production and business optimization and many others. Typical applica-
tions are, e.g., classification, pattern recognition, image processing, supervised and
unsupervised learning, statistical learning and community detection in graph based
data. By this, data analytics contributes fundamentally to the
field
of machine
learning and artificial intelligence.
There is tremendous demand for corresponding methods. By digitization of
industrial processes and the Internet of Things, huge amounts of data will be
collected in short periods of time. It is of high value to analyze this data for the
purpose of steering underlying production processes. Furthermore, social media and
Internet searches generate huge amounts of diverse data, which have to be scruti-
nized for meaningful conclusions. Furthermore, healthcare and medical research
rely on fast and efficient evaluation of data. It is estimated that in the near future
more than one megabyte of data per person will be created each second.
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