Open sourcing education for Data Engineering and Data Science
The fields of Data Engineering and Data Science have emerged in recent years as an exciting intersection between leading academic research and industry leaders, and include diverse, cutting-edge topics like distributed systems, machine learning, and artificial intelligence. While these topics are in...
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| Veröffentlicht in: | 2016 IEEE Frontiers in Education Conference (FIE) S. 1 |
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| 1. Verfasser: | |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.10.2016
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| Schlagworte: | |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The fields of Data Engineering and Data Science have emerged in recent years as an exciting intersection between leading academic research and industry leaders, and include diverse, cutting-edge topics like distributed systems, machine learning, and artificial intelligence. While these topics are interesting in their own right, perhaps the most compelling aspect of these fields is how the tools, which are in widespread use, have been developed by an Open Source community. Individuals across several universities and companies have worked together in a distributed fashion to build and improve the leading generation of data technologies. These Open Source principles have enabled the latest industry-adopted tools to constantly evolve at an incredible rate. One way for educators to keep pace with these developments and maintain advanced curriculum is to adopt the same collaborative principles used in open source. At the Insight Data Fellowship, we've used this open source model to provide immediate feedback and drive our curriculum forward, while fostering a culture of independence and curiosity. This session will show Engineering educators how to use open source principles and tools to develop their own curriculum. |
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| DOI: | 10.1109/FIE.2016.7757517 |