Nino Antulov-Fantulin: Katalogdaten im Frühjahrssemester 2023 |
| Name | Herr Dr. Nino Antulov-Fantulin |
| Lehrgebiet | Computer-gestüzte Sozialwissenschaften |
| Adresse | Computational Social Science ETH Zürich, STD F 4 Stampfenbachstrasse 48 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 61 57 |
| nino.antulov@gess.ethz.ch | |
| Departement | Geistes-, Sozial- und Staatswissenschaften |
| Beziehung | Privatdozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0585-38L | Data Science in Techno-Socio-Economic Systems  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 | 3 KP | 2V | D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Will be provided on a separate course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Substantial programming skills and knowledge of statistical methods are expected. We recommend this course for students in the 4th semester or above. Students need to present a new subject, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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