851-0585-38L  Data Science in Techno-Socio-Economic Systems

SemesterFrühjahrssemester 2021
DozierendeD. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarNumber 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



Lehrveranstaltungen

NummerTitelUmfangDozierende
851-0585-38 VData Science in Techno-Socio-Economic Systems24s Std.
Mo16-18HG D 7.1 »
D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite

Katalogdaten

KurzbeschreibungThis 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.
LernzielThe 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.
InhaltWill be provided on a separate course webpage.
SkriptSlides will be provided.
LiteraturGrus, 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 / BesonderesGood programming skills and a good understanding of probability & statistics and calculus are expected.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte3 KP
PrüfendeD. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite
Formbenotete Semesterleistung
PrüfungsspracheEnglisch
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.

Lernmaterialien

Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

PlätzeMaximal 80
WartelisteBis 07.03.2021

Angeboten in

StudiengangBereichTyp
GESS Wissenschaft im Kontext (Science in Perspective)D-INFKWInformation
GESS Wissenschaft im Kontext (Science in Perspective)D-ITETWInformation
GESS Wissenschaft im Kontext (Science in Perspective)SoziologieWInformation
GESS Wissenschaft im Kontext (Science in Perspective)D-MTECWInformation
GESS Wissenschaft im Kontext (Science in Perspective)D-MAVTWInformation
GESS Wissenschaft im Kontext (Science in Perspective)D-PHYSWInformation
Integrated Building Systems MasterGESS Wissenschaft im KontextWInformation
Science, Technology, and Policy MasterWahlfächerWInformation