Avoiding a Data Science Winter by Keeping the Expectations Low

Authors

DOI:

https://doi.org/10.3991/ijac.v13i4.16933

Keywords:

data science, Artificial Intelligence, Machine Learning

Abstract


In the 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.

Author Biographies

Matthias Hofstetter, Institut Digital Enabling Berner Fachhochschule

Professor

Adamantios Koumpis, Institut Digital Enabling Berner Fachhochschule

Dozent

Kyriakos Chatzidimitriou, Aritstotle University of Thessaloniki

Kyriakos is an intelligent systems, data and software engineer as well as an indy researcher.

His research, development and business interests are in the broad area of Software Engineering and Machine Learning.

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Published

2020-12-15

How to Cite

Hofstetter, M., Koumpis, A., & Chatzidimitriou, K. (2020). Avoiding a Data Science Winter by Keeping the Expectations Low. International Journal of Advanced Corporate Learning (iJAC), 13(4), pp. 4–12. https://doi.org/10.3991/ijac.v13i4.16933

Issue

Vol. 13 No. 4 (2020)

Section

Papers

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