Avoiding a Data Science Winter by Keeping the Expectations Low
Version
Published
Date Issued
2020-12-15
Author(s)
Type
Article
Language
English
Abstract
In this paper we present and discuss some aspects related to what we consider as some of the most important corporate challenges of Data Science, AI and Machine Learning regarding both human talents and business. We examine the case of a discussion that took place over Quora and in particular we focus on an answer we have selected as indicative of a potentially threatening situation for the sustainable development of the Data Science, AI and Machine Learning disciplines as well as the growth of the respective demand and supply sides and the corresponding ecosystem these form. We then make an attempt to examine the setting by means of analyzing the case, using as our guide the provided narrative.
Subjects
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
Publisher DOI
Journal
International Journal: Advanced Corporate Learning
ISSN
1867-5565
Publisher URL
Volume
13
Issue
4
Publisher
International Association of Online Engineering (IAOE)
Submitter
KoumpisA
Citation apa
Hofstetter, M., Koumpis, A., & Chatzidimitriou, K. (2020). Avoiding a Data Science Winter by Keeping the Expectations Low. In International Journal: Advanced Corporate Learning (Vol. 13, Issue 4). International Association of Online Engineering (IAOE). https://doi.org/10.24451/arbor.13901
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AI Winter - Hofstetter et al 16933-61939-1-PB.pdf
License
Attribution 4.0 International
Version
published
Size
818.77 KB
Format
Adobe PDF
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