Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3174
Title: Lessons learned from challenging data science case studies
Authors : Stockinger, Kurt
Braschler, Martin
Stadelmann, Thilo
et. al : No
Published in : Applied data science : lessons learned for the data-driven business
Pages : 447
Pages to: 465
Editors of the parent work: Braschler, Martin
Stadelmann, Thilo
Stockinger, Kurt
Publisher / Ed. Institution : Springer
Publisher / Ed. Institution: Cham
Issue Date: 14-Jun-2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: Editorial review
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.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Book Part
DOI : 10.21256/zhaw-3174
10.1007/978-3-030-11821-1_24
ISBN: 978-3-030-11821-1
978-3-030-11820-4
URI: https://digitalcollection.zhaw.ch/handle/11475/17424
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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