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© 2020

Guide to Intelligent Data Science

How to Intelligently Make Use of Real Data

  • 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

Textbook

Part of the Texts in Computer Science book series (TCS)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 1-14
  3. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 15-23
  4. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 25-32
  5. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 33-83
  6. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 85-126
  7. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 127-156
  8. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 157-218
  9. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 219-271
  10. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 273-317
  11. Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo
    Pages 319-328
  12. Back Matter
    Pages 329-420

About this book

Introduction

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.

Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining at the University of Konstanz. Prof. Dr. Christian Borgelt is Professor for Data Science at the Paris Lodron University of Salzburg. Prof. Dr. Frank Höppner is Professor of Information Engineering at Ostfalia University of Applied Sciences. 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. Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG.

Keywords

KNIME bioinformatics calculus classification cognition data analysis databases knowledge modeling pattern recognition statistics

Authors and affiliations

  1. 1.Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
  2. 2.Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  3. 3.Department of Computer ScienceOstfalia University of Applied SciencesWolfenbüttelGermany
  4. 4.Department of Computer ScienceOstfalia University of Applied SciencesWolfenbüttelGermany
  5. 5.KNIME AGZurichSwitzerland

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.

Bibliographic information

  • 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
  • Series Abbreviated Title Texts in Computer Science (formerly: Graduate Texts Comp. Sc.)
  • DOI https://doi.org/10.1007/978-3-030-45574-3
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-3-030-45573-6
  • Softcover ISBN 978-3-030-45576-7
  • eBook ISBN 978-3-030-45574-3
  • 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
    Big Data/Analytics