From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence.
Please note the information provided by the lecturers via e-mail.

Nino Antulov-Fantulin: Catalogue data in Spring Semester 2020

Name Dr. Nino Antulov-Fantulin
Address
Computational Social Science
ETH Zürich, STD F 4
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telephone+41 44 632 61 57
E-mailnino.antulov@gess.ethz.ch
DepartmentHumanities, Social and Political Sciences
RelationshipLecturer

NumberTitleECTSHoursLecturers
851-0585-38LData Science in Techno-Socio-Economic Systems Restricted registration - show details
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
3 credits3SN. Antulov-Fantulin
AbstractThis 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.
ObjectiveThe 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.
Prerequisites / NoticeGood programming skills and a good understanding of probability & statistics and calculus are expected.