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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: | 10.21256/zhaw-3174 10.1007/978-3-030-11821-1_24 |
| 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: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Applied Information Technology (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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ADS_2019_LessonsLearned.pdf | preprint | 289.25 kB | Adobe PDF |  View/Open |
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