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
Part of the book series: Texts in Computer Science (TCS)
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Table of contents (10 chapters)
About this book
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 the subject.
- data analysis
- pattern recognition
Authors and Affiliations
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
Michael R. Berthold
Department of Computer Sciences, University of Salzburg, Salzburg, Austria
Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
Frank Höppner, Frank Klawonn
KNIME AG, Zurich, Switzerland
About the authors
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.
Book Title: Guide to Intelligent Data Science
Book Subtitle: How to Intelligently Make Use of Real Data
Authors: Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
Series Title: Texts in Computer Science
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-45573-6Published: 07 August 2020
Softcover ISBN: 978-3-030-45576-7Published: 08 August 2021
eBook ISBN: 978-3-030-45574-3Published: 06 August 2020
Series ISSN: 1868-0941
Series E-ISSN: 1868-095X
Edition Number: 2
Number of Pages: XIII, 420
Number of Illustrations: 57 b/w illustrations, 122 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Machine Learning, Data Analysis and Big Data