Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
01.10.2017
Verlag
Elsevier LTD, OxfordSeitenzahl
256
Maße (L/B/H)
23.5/19.1/2 cm
Gewicht
504 g
Sprache
Englisch
ISBN
978-0-12-803655-6
Theoretic Foundation of Predictive Data Analytics presents the latest in data science, an area that is penetrating into virtually every discipline of science, engineering, and medicine, and is a fast evolving field. Practitioners, researchers, and graduate students often have difficulty in understanding the foundation of data science.
In order to have a deep understanding of data science, a strong understanding of statistical analysis and machine learning is a must. This book introduces the commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms.
Presents an ideal guide for readers that want to go deep into the basics of statistics and probability and how it applies to data science
Illustrates the connection of widely used data analytics methods and statistical and computational principles
Presents applied examples from several disciplines including, but not limited to, computer science, engineering and medicine
Discusses extensive experimental results using real application datasets to demonstrate the performance of statistical and machine learning techniques
In order to have a deep understanding of data science, a strong understanding of statistical analysis and machine learning is a must. This book introduces the commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms.
Presents an ideal guide for readers that want to go deep into the basics of statistics and probability and how it applies to data science
Illustrates the connection of widely used data analytics methods and statistical and computational principles
Presents applied examples from several disciplines including, but not limited to, computer science, engineering and medicine
Discusses extensive experimental results using real application datasets to demonstrate the performance of statistical and machine learning techniques
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