Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

260072 VU Data Science for Physicists (2023S)

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

Kickoff Lecture: 09.03.2023 09:30 - 11:00 (Ludwig-Boltzmann-Hörsaal)

Lectures (Theory): Th 09:30-11:00 (Ludwig-Boltzmann-Hörsaal)

Practice (4 groups):
Group 1: Tuesday from 12.03.2023 till 20.06.2023 08.45-09.30 Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;
Group 2: Tuesday from 12.03.2023 till 20.06.2023 09.45-10.30 Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;
Group 3: Tuesday from 12.03.2023 till 20.06.2023 10.45-11.30 Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;
Gruop 4: Tuesday from 12.03.2023 till 20.06.2023 11.45-12.30 Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien

  • Thursday 09.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 16.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 23.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 30.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 20.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 27.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 04.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 11.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 25.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 01.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 15.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 22.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 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-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 provide students with an Introduction to the basic theoretical concepts and practical skills oriented towards Data Science in Physics. 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 (mostly traditional methods, while machine learning techniques will be discussed in the final couple of lectures).

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

Assessment and permitted materials

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

Students are also required to submit a minimum percentage of the weekly proposed exercises.

Minimum requirements and assessment criteria

Minimum requirements (before registration):
- Basic but solid knowledge of python coding (e.g., as obtained by the previous programming course in the Bachelor curriculum).

Please note also that UNEXCUSED ABSENCE from the kick-off lecture will lead to immediate deregistration.

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

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

Association in the course directory

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

Last modified: Tu 30.01.2024 17:46