Universität Wien

260072 VU Data Science for Physicists (2020S)

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

An/Abmeldung

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

Details

max. 75 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

es werden dazu 2 Übungsgruppen angeboten:
Fr 10:15-11:15 Uhr, Kurt-Gödel-HS
Fr 11:30-12:30 Uhr, Kurt-Gödel-HS
Bei der Vorbesprechung wird die Gruppeneinteilung vorgenommen.

  • Donnerstag 19.03. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien (Vorbesprechung)
  • Donnerstag 26.03. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 02.04. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 23.04. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 30.04. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 07.05. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 14.05. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 28.05. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 04.06. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 18.06. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
  • Donnerstag 25.06. 09:15 - 10:30 Christian-Doppler-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien

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-centred 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, eventually.

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, such as machine learning.

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

Art der Leistungskontrolle und erlaubte Hilfsmittel

The evaluation of the students takes place continuously, during the practical lectures (on Fridays).

Mindestanforderungen und Beurteilungsmaßstab

The assessment consists in:
- solving successfully the exercises assigned weekly during the practical lectures;
- public presentation of the results during the practical lectures.
- regular attendance at lectures

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

Zuordnung im Vorlesungsverzeichnis

DSC

Letzte Änderung: Mo 07.09.2020 15:21