Ebook: Data Analytics: Models and Algorithms for Intelligent Data Analysis

This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. 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. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, 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.

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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.

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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.

Content:
Front Matter....Pages i-ix
Introduction....Pages 1-3
Data and Relations....Pages 5-20
Data Preprocessing....Pages 21-34
Data Visualization....Pages 35-54
Correlation....Pages 55-61
Regression....Pages 63-78
Forecasting....Pages 79-83
Classification....Pages 85-101
Clustering....Pages 103-122
Brief Review of Some Optimization Methods....Pages 123-126
Solutions....Pages 127-129
Back Matter....Pages 131-137

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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.

Content:
Front Matter....Pages i-ix
Introduction....Pages 1-3
Data and Relations....Pages 5-20
Data Preprocessing....Pages 21-34
Data Visualization....Pages 35-54
Correlation....Pages 55-61
Regression....Pages 63-78
Forecasting....Pages 79-83
Classification....Pages 85-101
Clustering....Pages 103-122
Brief Review of Some Optimization Methods....Pages 123-126
Solutions....Pages 127-129
Back Matter....Pages 131-137
....