Avoiding a Data Science Winter by Keeping the Expectations Low

Hofstetter, Matthias; Koumpis, Adamantios; Chatzidimitriou, Kyriakos (2020). Avoiding a Data Science Winter by Keeping the Expectations Low International Journal: Advanced Corporate Learning, 13(4), pp. 4-12. International Association of Online Engineering (IAOE) 10.3991/ijac.v13i4.16933

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

Business School > Institute for Digital Enabling

Name:

Hofstetter, Matthias;
Koumpis, Adamantios and
Chatzidimitriou, Kyriakos

Subjects:

Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)

ISSN:

1867-5565

Publisher:

International Association of Online Engineering (IAOE)

Language:

English

Submitter:

Adamantios Koumpis

Date Deposited:

16 Dec 2020 12:20

Last Modified:

01 Oct 2021 02:18

Publisher DOI:

10.3991/ijac.v13i4.16933

Uncontrolled Keywords:

data science, Artificial Intelligence, Machine Learning

ARBOR DOI:

10.24451/arbor.13901

URI:

https://arbor.bfh.ch/id/eprint/13901

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