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Applied Data Science

Lessons Learned for the Data-Driven Business

  • Martin Braschler
  • Thilo Stadelmann
  • Kurt Stockinger
Book

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Thilo Stadelmann, Martin Braschler, Kurt Stockinger
      Pages 3-16
    3. Martin Braschler, Thilo Stadelmann, Kurt Stockinger
      Pages 17-29
    4. Thilo Stadelmann, Kurt Stockinger, Gundula Heinatz Bürki, Martin Braschler
      Pages 31-45
    5. Jürg Meierhofer, Thilo Stadelmann, Mark Cieliebak
      Pages 47-61
    6. Michael Widmer, Stefan Hegy
      Pages 63-78
  3. Use Cases

    1. Front Matter
      Pages 97-97
    2. Martin Braschler, Thilo Stadelmann, Kurt Stockinger
      Pages 99-100
    3. Michael L. Brodie
      Pages 101-130
    4. Michael L. Brodie
      Pages 131-160
    5. Markus Christen, Helene Blumer, Christian Hauser, Markus Huppenbauer
      Pages 161-180
    6. Marcel Dettling, Andreas Ruckstuhl
      Pages 181-203
    7. Thilo Stadelmann, Vasily Tolkachev, Beate Sick, Jan Stampfli, Oliver Dürr
      Pages 205-232
    8. Philipp Ackermann, Kurt Stockinger
      Pages 251-264
    9. Bernhard Tellenbach, Marc Rennhard, Remo Schweizer
      Pages 265-288
    10. Laura Rettig, Mourad Khayati, Philippe Cudré-Mauroux, Michał Piorkówski
      Pages 289-312
    11. Lukas Hollenstein, Lukas Lichtensteiger, Thilo Stadelmann, Mohammadreza Amirian, Lukas Budde, Jürg Meierhofer et al.
      Pages 313-331
    12. Melanie Geiger, Kurt Stockinger
      Pages 333-351
    13. Thomas Ott, Stefan Glüge, Richard Bödi, Peter Kauf
      Pages 371-386
    14. Wolfgang Breymann, Nils Bundi, Jonas Heitz, Johannes Micheler, Kurt Stockinger
      Pages 387-408
    15. Serge Bignens, Murat Sariyar, Ernst Hafen
      Pages 409-423
  4. Lessons Learned and Outlook

    1. Front Matter
      Pages 445-445
    2. Kurt Stockinger, Martin Braschler, Thilo Stadelmann
      Pages 447-465

About this book

Introduction

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other.  

With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are.  

The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want  to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

 


Keywords

Big Data Data Science Machine Learning Information Storage and Retrieval Data Analysis Information Systems Applications

Editors and affiliations

  • Martin Braschler
    • 1
  • Thilo Stadelmann
    • 2
  • Kurt Stockinger
    • 3
  1. 1.Inst. of Applied Information TechnologyZHAW Zurich University of Applied SciencesWinterthurSwitzerland
  2. 2.Inst. of Applied Information TechnologyZHAW Zurich University of Applied SciencesWinterthurSwitzerland
  3. 3.Inst. of Applied Information TechnologyZHAW Zurich University of Applied SciencesWinterthurSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-11821-1
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-11820-4
  • Online ISBN 978-3-030-11821-1
  • Buy this book on publisher's site
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