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La Serena School for Data Science: multidisciplinary hands-on education in the era of big data

Published online by Cambridge University Press:  23 December 2021

A. Bayo Open the ORCID record for A. Bayo [Opens in a new window] ,
M. J. Graham Open the ORCID record for M. J. Graham [Opens in a new window] ,
D. Norman ,
M. Cerda ,
G. Damke ,
A. Zenteno  and
C. Ibarlucea
A. Bayo
Affiliation:
Inst. de Física y Astronomía, Universidad de Valparaíso, Chile email: [email protected] Núcleo Milenio de Formación Planetaria (NPF)
M. J. Graham
Affiliation:
California Institute of Technology
D. Norman
Affiliation:
NSF’s OIR Lab, Tucson, AZ
M. Cerda
Affiliation:
Inst. of Biomedical Sciences & Center for Medical Informatics and Telemedicine, Universidad de Chile Biomedical Neuroscience Institute, Santiago, Chile
G. Damke
Affiliation:
Instituto de Investigación Multidisciplinar en Ciencia y Tecnología, Universidad de La Serena Association of Universities for Research in Astronomy (AURA)
A. Zenteno
Affiliation:
Association of Universities for Research in Astronomy (AURA)
C. Ibarlucea
Affiliation:
Association of Universities for Research in Astronomy (AURA)
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Abstract

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La Serena School for Data Science is a multidisciplinary program with six editions so far and a constant format: during 10-14 days, a group of ∼30 students (15 from the US, 15 from Chile and 1-3 from Caribbean countries) and ∼9 faculty gather in La Serena (Chile) to complete an intensive program in Data Science with emphasis in applications to astronomy and bio-sciences.

The students attend theoretical and hands-on sessions, and, since early on, they work in multidisciplinary groups with their “mentors” (from the faculty) on real data science problems. The SOC and LOC of the school have developed student selection guidelines to maximize diversity.

The program is very successful as proven by the high over-subscription rate (factor 5-8) and the plethora of positive testimony, not only from alumni, but also from current and former faculty that keep in contact with them.

Keywords

astroinformaticsstatisticsdata basessurveysmachine learningbig data
Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S367: Education and Heritage in the Era of Big Data in Astronomy , December 2019 , pp. 458 - 460
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

References

Cabrera-Vives, G. Reyes, I., Förster, F., Estévez, P. A., Maureira, JC. 2017, Apj, 836, 97 CrossRefGoogle Scholar