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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02247 |
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| _version_ | 1866910683655831552 |
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| author | Bayo, A. Mesa, V. Damke, G. Cerda, M. Graham, M. J. Norman, D. Forster, F. Ibarlucea, C. Monsalves, N. |
| author_facet | Bayo, A. Mesa, V. Damke, G. Cerda, M. Graham, M. J. Norman, D. Forster, F. Ibarlucea, C. Monsalves, N. |
| contents | The worlds of Data Science (including big and/or federated data, machine learning, etc) and Astrophysics started merging almost two decades ago. For instance, around 2005, international initiatives such as the Virtual Observatory framework rose to standardize the way we publish and transfer data, enabling new tools such as VOSA (SED Virtual Observatory Analyzer) to come to existence and remain relevant today. More recently, new facilities like the Vera Rubin Observatory, serve as motivation to develop efficient and extremely fast (very often deep learning based) methodologies in order to fully exploit the informational content of the vast Legacy Survey of Space and Time (LSST) dataset. However, fundamental changes in the way we explore and analyze data cannot permeate in the "astrophysical sociology and idiosyncrasy" without adequate training. In this talk, I will focus on one specific initiative that has been extremely successful and is based on "learning by doing": the La Serena School for Data Science. I will also briefly touch on a different successful approach: a series of schools organized by the Spanish Virtual Observatory. The common denominator among the two kinds of schools is to present the students with real scientific problems that benefit from the concepts / methodologies taught. On the other hand, the demographics targeted by both initiatives vary significantly and can represent examples of two "flavours" to be followed by others. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | La Serena School for Data Science and the Spanish Virtual Observatory Schools: Initiatives Based on Hands on Experience Bayo, A. Mesa, V. Damke, G. Cerda, M. Graham, M. J. Norman, D. Forster, F. Ibarlucea, C. Monsalves, N. Instrumentation and Methods for Astrophysics Physics Education The worlds of Data Science (including big and/or federated data, machine learning, etc) and Astrophysics started merging almost two decades ago. For instance, around 2005, international initiatives such as the Virtual Observatory framework rose to standardize the way we publish and transfer data, enabling new tools such as VOSA (SED Virtual Observatory Analyzer) to come to existence and remain relevant today. More recently, new facilities like the Vera Rubin Observatory, serve as motivation to develop efficient and extremely fast (very often deep learning based) methodologies in order to fully exploit the informational content of the vast Legacy Survey of Space and Time (LSST) dataset. However, fundamental changes in the way we explore and analyze data cannot permeate in the "astrophysical sociology and idiosyncrasy" without adequate training. In this talk, I will focus on one specific initiative that has been extremely successful and is based on "learning by doing": the La Serena School for Data Science. I will also briefly touch on a different successful approach: a series of schools organized by the Spanish Virtual Observatory. The common denominator among the two kinds of schools is to present the students with real scientific problems that benefit from the concepts / methodologies taught. On the other hand, the demographics targeted by both initiatives vary significantly and can represent examples of two "flavours" to be followed by others. |
| title | La Serena School for Data Science and the Spanish Virtual Observatory Schools: Initiatives Based on Hands on Experience |
| topic | Instrumentation and Methods for Astrophysics Physics Education |
| url | https://arxiv.org/abs/2411.02247 |