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Data Science in Techno-Socio-Economic Systems
Last Updated: 2026-02-05 16:38:15
Abstract
This course introduces how techno-socio-economic systems in our complex society can be better understood with techniques and tools of data science. Students shall learn how the fundamentals of data science are used to give insights into the research of complexity science, computational social science, economics, finance, and others.
Objective
The goal of this course is to introduce students to data science methods that help understanding of complex systems, often encountered in techno-socio-economic settings. By the end of the course, the students will be able to: 1. Formulate testable hypotheses about techno-socio-economic systems. 2. Apply methods from statistics, data science, or machine learning to test these hypotheses. 3. Presenting their research findings clearly, using appropriate visualisations and reporting standards.
Content
Will be provided on a separate course webpage.
Resources
Lecture Notes
Slides will be provided.
Literature
Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media. https://dl.acm.org/doi/10.5555/2904392 Mehta, P., Bukov, M., Wang, C. H., Day, A. G., Richardson, C., Fisher, C. K., & Schwab, D. J. (2019). A high-bias, low-variance introduction to machine learning for physicists. Physics reports, 810, 1-124. https://www.sciencedirect.com/science/article/pii/S0370157319300766 Chakrabarti, A., Bakar, K., & Chakraborti, A. (2023). Data Science for Complex Systems. Cambridge: Cambridge University Press. doi:10.1017/9781108953597 Link Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press. https://www.databookuw.com/ Further literature will be recommended in the lectures.
Learning Materials (Links)
- Main link
- Moodle
General Information
- Language
- English
- Levels
- BSC , DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Signup End
- 12.02.2024
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Data Science in Techno-Socio-Economic Systems |
|
24 h semesterly |
Offered In
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Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
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Type A: Enhancement of Reflection Competence (SiP courses are recommended for bachelor students after their first-year examination and for all master- or doctoral students. All SiP courses are listed in Type A. Courses listed under Type B are only recommendations for enrollment for specific departments.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses. Particularly relevant for students interested in those subjects. All these courses are also listed under the category “Typ A”, and every student can enroll in these courses.)
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Electives (The entire course programs of ETH Zurich and the University of Zurich are open to the students to individual selection. The students have themselves to check whether they meet the admission requirements for a course.)
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