Название: Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction, 4th Edition
Автор: Thomas A. Runkler
Издательство: Springer
Год: 2025
Страниц: 197
Язык: английский
Формат: pdf (true), epub
Размер: 17.0 MB

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. The new edition integrates themes such as Word Embeddings, Transformer Models, and Generative AI among the contents and offers new exercises in addition. 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.

The term data mining dates back to the 1980s. The goal of data mining is to extract knowledge from data. In this context, knowledge is defined as interesting patterns that are generally valid, novel, useful, and understandable to humans. Whether or not the extracted patterns are interesting depends on the particular application and needs to be verified by application experts. Based on expert feedback the knowledge extraction process is often interactively refined. The term data analytics first appeared in the early 2000s. Data analytics is defined as the application of computer systems to the analysis of large data sets for the support of decisions. Data analytics is a very interdisciplinary field that has adopted aspects from many other scientific disciplines such as statistics, machine learning, pattern recognition, system theory, operations research, or Artificial Intelligence.

Typical data analysis projects can be divided into several phases. Data are assessed and selected, cleaned and filtered, visualized and analyzed, and the analysis results are finally interpreted and evaluated. The knowledge discovery in databases (KDD) process comprises the six phases selection, preprocessing, transformation, data mining, interpretation, and evaluation. The cross industry standard process for data mining (CRISP-DM) comprises the six phases business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For simplicity we distinguish only four phases here: preparation, preprocessing, analysis, and postprocessing. The main focus of this book is data preprocessing and data analysis. The chapters are structured according to the main methods of preprocessing and analysis: data and relations, data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering.

This book gives a clear and concise overview of the most important methods and algorithms of data analysis. It enables the reader to gain a complete and comprehensive understanding of data analysis, to apply data analysis methods to own projects, and to contribute to the progress of the field. A large number a software tools for data mining are available today. Popular commercial or free software tools include Alteryx, AutoML, AWS Sagemaker, Databricks, KNIME, MATLAB, Orange, Python, R, Rapid Miner, SAS, Spark, Splunk, SPSS, or Tableau.

This 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. The book is structured according to typical practical data analytics projects. Only basic mathematics is required.

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