Universität Wien
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260072 VU Data Science for Physicists (2026S)

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

Lecture (VO):
Time: Thursdays (weekly) from March 5, 2026, to June 25, 2026, 09:30–11:00 AM
Location: Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, Ground Floor (EG), 1090 Vienna

Exercise Groups (UE):
Time: Tuesdays (weekly) from March 10, 2026, to June 23, 2026
Group 1: 09:00–09:45 Uhr
Group 2: 09:50–10:35 Uhr
Group 3: 10:45–11:30 Uhr
Location: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, Ground Floor (EG), 1090 Vienna

Exams (Tests):
Exams will be held during the regular lecture time slot. There are two exams: one mid-semester and one at the end of the term.

Exam 1:
Main Date: Thursday, April 30, 2026, 09:30–11:00 AM (Ludwig-Boltzmann-Hörsaal)
Make-up Date: Tuesday, May 5, 2026, 09:30–11:00 AM (Kurt-Gödel-Hörsaal)

Exam 2:
Main Date: Thursday, June 25, 2026, 09:30–11:00 AM (Ludwig-Boltzmann-Hörsaal)
Make-up Date: Tuesday, June 30, 2026, 09:30–11:00 AM (Kurt-Gödel-Hörsaal)

Important Note on Participation: For each exam, you may only attend one of the two provided dates. It is not possible to attend both dates to improve your grade. The make-up date (substitute date) is intended exclusively for students who were unable to participate in the main exam date (e.g., due to illness).

  • Thursday 05.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 19.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 26.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 16.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 23.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 30.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 07.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 21.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 28.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 11.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 18.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Thursday 25.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien

Information

Aims, contents and method of the course

Data science is fundamental for machine-learning applications and artificial intelligence. Considering the massive scientific, societal, and economic impact of this field, data science is one of the most critical skills needed to succeed in future science and industry. As a physicist, it is imperative to gain an understanding of data science that goes beyond buzzwords.

The staggering growth of computational power and the associated creation of large databases make data one of the most valuable resources of our century. Information stored in such large databases can be extracted by data science methods—that is, by combining statistics with advanced computational methods, including machine learning.

The course focuses on state-of-the-art data science methods in physics. We will discuss a combination of interdisciplinary activities, such as scientific, statistical, and computational tools, required in science as well as industry. This course aims to provide students with an introduction to basic theoretical concepts and practical skills oriented toward data science in physics. Specifically, the lectures cover the following topics:

Syllabus:
0 Introduction -- General information; What is data science in physics?
1 Linear Algebra -- Matrix decompositions, eigenvalue problems, and Principal Component Analysis.
2 Differential Equations -- Finite difference, integrating first-order and partial differential equations.
3 Fitting and Testing -- Regression methods and model testing.
4 Optimization -- Local and global optimization methods.
5 Statistics -- Statistical/Bayesian hypothesis testing and sampling methods.
6 Machine Learning -- Introduction to machine-learning and accuracy of machine-learning models.
7 Machine Learning -- Classification, the perception model, Bayesian classifiers, and trees.
8 Machine Learning -- Neural networks.
9 Machine Learning -- Kernel methods.

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

Assessment and permitted materials

The final grade is determined by a combination of continuous assessment during the practical exercise classes (Tuesdays) and two scheduled examinations (see dates above). To successfully complete the course and qualify for a grade, students must provide the following performances:

Exercise Preparation:
Students are required to solve and submit weekly exercise sets. A minimum of 50% of all tasks must be prepared to pass the course.

Active Presentation:
Each student must present at least one prepared exercise solution to the class. This includes explaining the methodology and results to fellow students and supervisors.

Scientific Discussion:
Active participation in technical discussions during the practical exercise classes (Tuesdays) is mandatory. Students must engage with questions from peers and supervisors during their presentations.

Theoretical Tests:
Students must sit for two written examinations (Mid-term and End-term), which evaluate theoretical comprehension of the lecture materials.

Minimum requirements and assessment criteria

1. Mandatory Requirements (Hard Requirements):
To pass the course, students must fulfill all three of the following criteria. Failure to meet any of these will result in a failing grade, regardless of total points:
- Exercise Submission: At least 50% of all weekly exercise tasks must be prepared and submitted on time. A submission without attendance does not count.
- Presentation: Every student must deliver at least one successful presentation of an exercise solution during the Tuesday practical classes.
- Minimum Score: A total score of more than 50 points must be achieved by the end of the semester.

2. Point Distribution
The maximum number of points achievable is 104 (100 base points + 4 bonus points).
Mid-term Test = 50 points
End-term Test = 50 points
Presentation Bonus = Up to 4 points
Total Possible = 104 points

3. Grading Scale
Grading is based on the total accumulated points (Tests + Bonus), provided all hard requirements are met:

Grade .... Point
1 ............ 88 – 104
2 ............ 75 – 87
3 ............ 62 – 74
4 ............ 50 – 61
5 ............ 0 – 49

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

Association in the course directory

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

Last modified: Mo 27.07.2026 09:27