| Dozent/in |
Prof. Dr. Ulrich Matter |
| Veranstaltungsart |
Vorlesung |
| Code |
HS261003 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Fr, 18.09.2026, 10:15 - 18:00 Uhr, 2.A10
Fr, 25.09.2026, 10:15 - 18:00 Uhr, Inseliquai 10 INE 220
Fr, 09.10.2026, 10:15 - 18:00 Uhr, HS 14
Fr, 04.12.2026, 14:15 - 18:00 Uhr, 3.B01 |
| Umfang |
Blockveranstaltung |
| Turnus |
Block Course |
| Inhalt |
This course introduces students to the concept of Big Data in the context of empirical economic research. Students learn about the computational constraints underlying Big Data Analytics and how to handle them in the statistical computing environment R (local and in the cloud). Revisiting basic statistical/econometric concepts, we look at each step of dealing with large data sets in empirical economic research (storage/import, transformation, visualization, aggregation). |
| Lernziele |
1) Students will know the concept of Big Data in the context of empirical economic research.
2) Students will understand the technical challenges of Big Data Analytics and how to practically deal with them.
3) Students will know how to apply the relevant R packages and programming practices to effectively and efficiently handle large data sets. |
| Voraussetzungen |
"Causal Analysis" and "Introduction to Computer Science and Programming" mandatory. "Data Science Toolkits and Architectures" recommended. |
| Sprache |
Englisch |
| Anmeldung |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 31 August – 13 September 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17903616235 |
| Leistungsnachweis |
In addition to the presentation, the code and technical report of the presented project will be evaluated.
***IMPORTANT*** To receive course credit and earn academic credits, registration via the Uni Portal from 31 August (starting at 9:00 a.m.) – 13 September 2026 is MANDATORY.
Late registrations and withdrawals will not be accepted. Once the registration period has ended, participation in the course is MANDATORY. If the course is not completed without a valid reason and without proper withdrawal (including supporting documentation; see the Exam Guidelines), the course will be considered failed (grade 1). |
| Abschlussform / Credits |
Individual/group presentation; written paper / 3 Credits
|
| Hörer-/innen |
Ja |
| Kontakt |
ulrich.matter@unisg.ch |
| Literatur |
Mandatory reading:
Matter, U. (2023). Big data analytics: a guide to data science practitioners making the transition to big data. CRC Press. FL: Boca Raton.
https://umatter.github.io/BigData/Wickham, Hadley (2019): Advanced R. Second Edition, CRC Press, FL: Boca Raton.
https://adv-r.hadley.nz/
|