Data Science Education: We're Missing the Boat, Again
| Autor: | Michael J. Franklin, Tim Kraska, Jeffrey D. Ullman, Laura M. Haas, Bill Howe |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Upstream (petroleum industry)
Reproducibility Computer science business.industry Data management 05 social sciences 050301 education Statistical model 02 engineering and technology Data science Data modeling Information engineering Data quality 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business 0503 education Anecdotal evidence |
| Zdroj: | ICDE |
| DOI: | 10.1109/icde.2017.215 |
| Popis: | In the first wave of data science education programs, data engineering topics (systems, scalable algorithms, data management, integration) tended to be de-emphasized in favor of machine learning and statistical modeling. The anecdotal evidence suggests this was a mistake: data scientists report spending most of their time grappling with data far upstream of modeling activities. A second wave of data science education is emerging, one with increased emphasis on practical issues in ethics, legal compliance, scientific reproducibility, data quality, and algorithmic bias. The data engineering community has a second chance to influence these programs beyond just providing a set of tools. In this panel, we'll discuss the role of data engineering in data science education programs, and how best to capitalize on emerging opportunities in this space. |
| Databáze: | OpenAIRE |
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