851-0585-38L Data Science in Techno-Socio-Economic Systems
| Semester | Spring Semester 2021 |
| Lecturers | D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Number of participants limited to 80 This course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations. Particularly suitable for students of D-INFK, D-ITET, D-MAVT, D-MTEC, D-PHYS |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 851-0585-38 V | Data Science in Techno-Socio-Economic Systems | 24s hrs |
| D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite |
Catalogue data
| 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. |
| Learning 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. |
| 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. |
| Prerequisites / Notice | Good programming skills and a good understanding of probability & statistics and calculus are expected. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 3 credits |
| Examiners | D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| Places | 80 at the most |
| Waiting list | until 07.03.2021 |
