Universität Wien FIND

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260072 VU Data Science for Physicists (2021S)

5.00 ECTS (3.00 SWS), SPL 26 - Physik
Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine

Vorlesungsanteil: DO wtl von 11.03.2021 bis 24.06.2021 09.00-10.15 Uhr, digital
es werden dazu 2 Übungsgruppen angeboten:
Di 08:30-09:30 Uhr, digital
Di 09:45-10:45 Uhr, digital
Bei der Vorbesprechung wird die Gruppeneinteilung vorgenommen.


Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course focuses on the application of Data Science methods in Physics, that is the combination of interdisciplinary activities (such as scientific, statistical and computational tools) required to elaborate data-centered analysis on relevant physical quantities. Data Science is a topic of increasing interest in the scientific community, due to the growing power of modern computational machines and the associated creation of large databases: The valuable information stored in such large databases can be extracted by Data Science methods, i.e., by combining statistics with advanced computational methods, including machine learning.

This course aims to guide students through the basic theoretical concepts regarding Data Science in Physics, and to provide them with the ability to successfully face practical applications in this field. Specifically, the lectures cover the following topics: (i) collection and manipulation of data via computational tools (mostly in python environments), (ii) effective visualization of relevant information extracted from data, (iii) scientific analysis and physical interpretation of data, (iv) advanced computational techniques.

The course is structured in theoretical lectures (on Thursdays), followed by practical lectures (on Tuesdays).

Art der Leistungskontrolle und erlaubte Hilfsmittel

The evaluation of the students takes place continuously, during the practical lectures (on Tuesdays), and by means of Mid-term and End-term tests.

Mindestanforderungen und Beurteilungsmaßstab

Minimum requirements (before registration):
- Basic but solid knowledge of python coding (e.g., as obtained by the programming course in the Bachelor curriculum).
Please note also that UNEXCUSED ABSENCE from the kick-off lecture will lead to immediate deregistration.

Prüfungsstoff

At the end of the course, the students are expected to be familiar with the topic discussed during lectures and to be able to collect data from unstructured sources, to store and efficiently manipulate data, to visually represent the relevant information, to perform rigorous physical interpretation, to reproduce simple machine learning models.

Literatur

S. L. Brunton, and J. N. Kutz, Cambridge University Press (2019), DOI:10.1017/9781108380690
https://doi.org/10.1017/9781108380690

P. Mehta, et al., Physics Reports 810, 1-124 (2019), DOI:10.1016/j.physrep.2019.03.001
https://doi.org/10.1016/j.physrep.2019.03.001

M. Nielsen, "Neural Networks and Deep Learning", Determination Press (2015),
http://neuralnetworksanddeeplearning.com/

Zuordnung im Vorlesungsverzeichnis

DSC, UF MA PHYS 01a, UF MA PHYS 01b

Letzte Änderung: Di 23.02.2021 14:08