Universität Wien
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260072 VU Data Science for Physicists (2020S)

5.00 ECTS (3.00 SWS), SPL 26 - Physik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 75 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

**** Major changes due to switching to e-learning mode:
**** Lectures take place online (BBBBN Moodle) on Fridays, from 10:15 to 12:30. Attendance is not required but succesfull recording of lectures is not guaranteed. For the updated list of lecture dates, see the information uploaded on Moodle.
**** Minimum requirement to pass the course: submission of 50% of weekly assigned exercises, successfully pass one of the two tests.
**** Mid-term and end-term test take place online, during lecture time.
*** Other information are maintained as during the frontal lecture phase, otherwise explicitly reported.

The exercises and testing will be organized in 2 separated groups:
Fr 10:15-11:15, Kurt-Gödel-HS
Fr 11:30-12:30, Kurt-Gödel-HS
The grouping takes place at the preliminary meeting (19/3/2020).

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

Information

Aims, contents and method of the course

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).

Assessment and permitted materials

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

Minimum requirements and assessment criteria

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

Examination topics

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.

Reading list

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

Association in the course directory

DSC

Last modified: Mo 07.09.2020 15:21