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Teaching machine learning and data literacy to students of logistics using Jupyter Notebooks
Citation Link: https://doi.org/10.15480/882.2943
Publikationstyp
Conference Paper
Date Issued
2020-09
Sprache
English
Author(s)
Herausgeber*innen
TORE-DOI
TORE-URI
First published in
Number in series
P-308
Start Page
365
End Page
366
Citation
Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (DELFI 2020)
Contribution to Conference
Scopus ID
Publisher
Gesellschaft für Informatik e.V.
Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks.
Subjects
Jupyter Notebooks
Code Literacy
Data Literacy
Machine Learning
Data Science
Logistics
Supply Chain
DDC Class
004: Informatik
370: Erziehung, Schul- und Bildungswesen
Funding(s)
More Funding Information
Bundesministerium für Bildung und Forschung (BMBF)
Publication version
publishedVersion
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365 DELFI2020_paper_35.pdf
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