Multivariate Distributions and Moments
Probability theory provides mathematical laws for randomness and is hence an essential tool for quantitative analysis of nondeterministic or noisy data. It allows the description of complex systems when only partial knowledge of the state is available. Fo
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ndamentals 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
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Rudolf Mathar 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
Emilio Balda Institute for Theoretical Information Technology 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)
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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
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