| Description |
1 online resource |
| Contents |
Introduction -- Data and relations -- Data preprocessing -- Data visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering -- Brief review of some optimization methods |
| Summary |
This book is a comprehensive introduction to the methods and algorithms and approaches 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 numerous courses at the Technical University of Munich, in short courses at several other universities, and in tutorials at scientific conferences. 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 data analytics for engineering, computer science and math Practitioners working on data analytics projects The Author Thomas Runkler is doing research at Siemens Corporate Technology in Munich and teaching data analytics and machine learning at the Technical University of Munich |
| Analysis |
Computer science |
|
Data structures (Computer science) |
|
Data mining |
| Bibliography |
Includes bibliographical references and index |
| In |
Springer eBooks |
| Subject |
Data mining.
|
|
Data structures (Computer science)
|
|
Informatique.
|
|
Data mining
|
|
Data structures (Computer science)
|
| Form |
Electronic book
|
| ISBN |
9783834825896 |
|
3834825891 |
|
3834825883 |
|
9783834825889 |
|
