Lessons learned from challenging data science case studies

Stockinger, Kurt; Braschler, Martin; Stadelmann, Thilo

doi:10.1007/978-3-030-11821-1_24

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3174

Publication type:	Book part
Type of review:	Editorial review
Title:	Lessons learned from challenging data science case studies
Authors:	Stockinger, Kurt Braschler, Martin Stadelmann, Thilo
et. al:	No
DOI:	10.1007/978-3-030-11821-1_24 10.21256/zhaw-3174
Published in:	Applied data science : lessons learned for the data-driven business
Editors of the parent work:	Braschler, Martin Stadelmann, Thilo Stockinger, Kurt
Page(s):	447
Pages to:	465
Issue Date:	14-Jun-2019
Publisher / Ed. Institution:	Springer
Publisher / Ed. Institution:	Cham
ISBN:	978-3-030-11821-1 978-3-030-11820-4
Language:	English
Subjects:	Conclusion; Data science; Digital transformation; Artificial intelligence; Future; Society; Business; Summary
Subject (DDC):	005: Computer programming, programs and data
Abstract:	In this chapter, we revisit the conclusions and lessons learned of the chapters presented in Part II of this book and analyze them systematically. The goal of the chapter is threefold: firstly, it serves as a directory to the individual chapters, allowing readers to identify which chapters to focus on when they are interested either in a certain stage of the knowledge discovery process or in a certain data science method or application area. Secondly, the chapter serves as a digested, systematic summary of data science lessons that are relevant for data science practitioners. And lastly, we reflect on the perceptions of a broader public towards the methods and tools that we covered in this book and dare to give an outlook towards the future developments that will be influenced by them.
URI:	https://digitalcollection.zhaw.ch/handle/11475/17424
Fulltext version:	Submitted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Published as part of the ZHAW project:	Complexity 4.0 PANOPTES DaCoMo - Data-Driven Condition Monitoring Market Monitoring Large Scale Data-Driven Financial Risk Modelling
Appears in collections:	Publikationen School of Engineering

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Stockinger, K., Braschler, M., & Stadelmann, T. (2019). Lessons learned from challenging data science case studies. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 447–465). Springer. https://doi.org/10.1007/978-3-030-11821-1_24

Stockinger, K., Braschler, M. and Stadelmann, T. (2019) ‘Lessons learned from challenging data science case studies’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 447–465. Available at: https://doi.org/10.1007/978-3-030-11821-1_24.

K. Stockinger, M. Braschler, and T. Stadelmann, “Lessons learned from challenging data science case studies,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 447–465. doi: 10.1007/978-3-030-11821-1_24.

STOCKINGER, Kurt, Martin BRASCHLER und Thilo STADELMANN, 2019. Lessons learned from challenging data science case studies. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 447–465. ISBN 978-3-030-11821-1

Stockinger, Kurt, Martin Braschler, and Thilo Stadelmann. 2019. “Lessons Learned from Challenging Data Science Case Studies.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 447–65. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_24.

Stockinger, Kurt, et al. “Lessons Learned from Challenging Data Science Case Studies.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 447–65, https://doi.org/10.1007/978-3-030-11821-1_24.