This is a systematic overview and classification of tasks in data analysis, methods to solve them and typical problems encountered. The book combines views from classical and non-classical statistics like Bayesian inference and robust statistics.

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a need-to-have tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a need to use, need to keep resource following one's exploration of thesubject.

Supplies a broad-range of perspectives on data science, providing readers with a comprehensive account of the field Presents a focus on practical aspects, in addition to a detailed description of the theory Emphasizes the common pitfalls that often lead to incorrect or insufficient analyses, to help readers avoid such errors Includes extensive hands-on examples, enabling readers to gain further insight into the topic

Autorentext

Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining in the Department of Computer Science at the University of Konstanz, Germany. Prof. Dr. Christian Borgelt is Professor for Data Science in the departments of Mathematics and Computer Sciences at the Paris Lodron University of Salzburg, Austria; he also co-authored the Springer textbook, Computational Intelligence. Prof. Dr. Frank Höppner is Professor of Information Engineering in the Department of Computer Science at Ostfalia University of Applied Sciences, Wolfenbüttel, Germany. Prof. Dr. Frank Klawonn is Professor for Data Analysis and Pattern Recognition at the same institution and head of the Biostatistics Group at the Helmholtz Centre for Infection Research, Braunschweig, Germany; he has authored the Springer textbook, Introduction to Computer Graphics. Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG, Zurich, Switzerland.

Inhalt

Introduction.- Practical Data Analysis: An Example.- Project Understanding.- Data Understanding.- Principles of Modeling.- Data Preparation.- Finding Patterns.- Finding Explanations.- Finding Predictors.- Evaluation and Deployment.- The Labelling Problem.- Appendix A: Statistics.- Appendix B: KNIME.