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Data Science in Techno-Socio-Economic Systems
Last Updated: 2026-02-05 16:08:40
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 qualify students with knowledge on data science to better understand techno-socio-economic systems in our complex societies. This course aims to make students capable of applying the most appropriate and effective techniques of data science under different application scenarios. The course aims to engage students in exciting state-of-the-art scientific tools, methods and techniques of data science. In particular, lectures will be divided into research talks and tutorials. The course shall increase the awareness level of students of the importance of interdisciplinary research. Finally, students have the opportunity to develop their own data science skills based on a data challenge task, they have to solve, deliver and present at the end of the course.
Content
Will be provided on a separate course webpage.
Resources
Lecture Notes
Slides will be provided.
Literature
Grus, Joel. "Data Science from Scratch: First Principles with Python". O'Reilly Media, 2019. https://dl.acm.org/doi/10.5555/2904392 "A high-bias, low-variance introduction to machine learning for physicists" https://www.sciencedirect.com/science/article/pii/S0370157319300766 Applications to Techno-Socio-Economic Systems: "The hidden geometry of complex, network-driven contagion phenomena" (relevant for modeling pandemic spread) https://science.sciencemag.org/content/342/6164/1337 "A network framework of cultural history" https://science.sciencemag.org/content/345/6196/558 "Science of science" https://science.sciencemag.org/content/359/6379/eaao0185.abstract "Generalized network dismantling" https://www.pnas.org/content/116/14/6554 Further literature will be recommended in the lectures.
General Information
- Language
- English
- Levels
- BSC , DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 130
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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