Data Analytics Models and Algorithms for Intelligent Data Analysis /
Runkler, Thomas A.
Data Analytics Models and Algorithms for Intelligent Data Analysis / [electronic resource] : by Thomas A. Runkler. - 3rd ed. 2020. - XII, 161 p. 72 illus. Textbook for German language market. online resource.
Introduction -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering.
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens. Content • Data Analytics • Data and Relations • Data Preprocessing • Data Visualization • Correlation • Regression • Forecasting • Classification • Clustering Target Groups Students of computer science, mathematics and engineering Data analytics practitioners The Author Thomas A. Runkler is Principal Research Scientist atSiemens Corporate Technology and Professor for Computer Science at the Technical University of Munich.
9783658297794
10.1007/978-3-658-29779-4 doi
Data mining.
Data structures (Computer science).
Information theory.
Data Mining and Knowledge Discovery.
Data Structures and Information Theory.
QA76.9.D343
006.312
Data Analytics Models and Algorithms for Intelligent Data Analysis / [electronic resource] : by Thomas A. Runkler. - 3rd ed. 2020. - XII, 161 p. 72 illus. Textbook for German language market. online resource.
Introduction -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering.
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens. Content • Data Analytics • Data and Relations • Data Preprocessing • Data Visualization • Correlation • Regression • Forecasting • Classification • Clustering Target Groups Students of computer science, mathematics and engineering Data analytics practitioners The Author Thomas A. Runkler is Principal Research Scientist atSiemens Corporate Technology and Professor for Computer Science at the Technical University of Munich.
9783658297794
10.1007/978-3-658-29779-4 doi
Data mining.
Data structures (Computer science).
Information theory.
Data Mining and Knowledge Discovery.
Data Structures and Information Theory.
QA76.9.D343
006.312