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Data Analytics Models and Algorithms for Intelligent Data Analysis /
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. T...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiesbaden :
Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg,
2016.
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| Edición: | 2nd ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-658-14075-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220120215025.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 160726s2016 gw | s |||| 0|eng d | ||
| 020 | |a 9783658140755 |9 978-3-658-14075-5 | ||
| 024 | 7 | |a 10.1007/978-3-658-14075-5 |2 doi | |
| 050 | 4 | |a QA76.9.D343 | |
| 072 | 7 | |a UNF |2 bicssc | |
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| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Runkler, Thomas A. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Data Analytics |h [electronic resource] : |b Models and Algorithms for Intelligent Data Analysis / |c by Thomas A. Runkler. |
| 250 | |a 2nd ed. 2016. | ||
| 264 | 1 | |a Wiesbaden : |b Springer Fachmedien Wiesbaden : |b Imprint: Springer Vieweg, |c 2016. | |
| 300 | |a XII, 150 p. 66 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Data Analytics -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering. | |
| 520 | |a 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 at Siemens Corporate Technology and Professor for Computer Science at the Technical University of Munich. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Artificial intelligence-Data processing. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Data Science. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783658140748 |
| 776 | 0 | 8 | |i Printed edition: |z 9783658140762 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-658-14075-5 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||