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  • 2017.12_dtw

    Rights statement: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published
Publication date11/12/2017
Host publicationCloud Computing Technology and Science (CloudCom), 2017 IEEE International Conference on
PublisherIEEE
Pages357-362
Number of pages6
ISBN (electronic)9781538606926
ISBN (print)9781538606933
<mark>Original language</mark>English

Abstract

Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learned lessons that are of relevance to others teaching Data Science.

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©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.