Ab 2. November 2020 findet das Herbstsemester 2020 online statt. Ausnahmen: Veranstaltungen, die nur mit Präsenz vor Ort durchführbar sind.
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Christoph Stadtfeld: Katalogdaten im Frühjahrssemester 2019

NameHerr Prof. Dr. Christoph Stadtfeld
LehrgebietSoziale Netzwerke
Adresse
Professur für Soziale Netzwerke
ETH Zürich, WEP J 16
Weinbergstr.109
8092 Zürich
SWITZERLAND
Telefon+41 44 632 07 93
E-Mailchristoph.stadtfeld@ethz.ch
URLhttp://www.social-networks.ethz.ch/
DepartementGeistes-, Sozial- und Staatswissenschaften
BeziehungAusserordentlicher Professor

NummerTitelECTSUmfangDozierende
851-0252-04LBehavioral Studies Colloquium Information 0 KP2KU. Brandes, V. Amati, H.‑D. Daniel, D. Helbing, C. Hölscher, M. Kapur, R. Schubert, C. Stadtfeld, E. Stern
KurzbeschreibungThis colloquium offers an opportunity for students to discuss their ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It also offers an opportunity for students from other disciplines to discuss their research ideas in relation to behavioral science. The colloquium also features invited research talks.
LernzielStudents know and can apply autonomously up-to-date investigation methods and techniques in the behavioral sciences. They achieve the ability to develop their own ideas in the field and to communicate their ideas in oral presentations and in written papers. The credits will be obtained by a written report of approximately 10 pages.
InhaltThis colloquium offers an opportunity for students to discuss their ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It also offers an opportunity for students from other disciplines to discuss their ideas in so far as they have some relation to behavioral science. The possible research areas are wide and may include theoretical as well as empirical approaches in Social Psychology and Research on Higher Education, Sociology, Modeling and Simulation in Sociology, Decision Theory and Behavioral Game Theory, Economics, Research on Learning and Instruction, Cognitive Psychology and Cognitive Science. Ideally the students (from Bachelor, Master, Ph.D. and Post-Doc programs) have started to start work on their thesis or on any other term paper.
Course credit can be obtained either based on a talk in the colloquium plus a written essay, or by writing an essay about a topic related to one of the other talks in the course. Students interested in giving a talk should contact the course organizers (Ziegler, Kapur) before the first session of the semester. Priority will be given to advanced / doctoral students for oral presentations. The course credits will be obtained by a written report of approximately 10 pages. The colloquium also serves as a venue for invited talks by researchers from other universities and institutions related to behavioral and social sciences.
851-0252-06LIntroduction to Social Networks: Theory, Methods and Applications
This course is intended for students interested in data analysis and with basic knowledge of inferential statistics.
3 KP2GC. Stadtfeld, T. Elmer, A. Vörös
KurzbeschreibungHumans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines.
LernzielThe aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3).
In particular, at the end of the course students will
- Know the fundamental theories in social networks research (1)
- Understand core concepts of social networks and their relevance in different contexts (1, 3)
- Be able to describe and visualize networks data in the R environment (2)
- Understand differences regarding analysis and collection of network data and other type of survey data (2)
- Know state-of-the-art inferential statistical methods and how they are used in R (2)
- Be familiar with the core empirical studies in social networks research (2, 3)
- Know how network methods can be employed in a variety of scientific disciplines (3)