| Dozent/in |
Prof. Dr. Ulrich Matter |
| Veranstaltungsart |
Vorlesung |
| Code |
FS221195 |
| Semester |
Frühjahrssemester 2022 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Do, 03.03.2022, 14:15 - 18:00 Uhr, HS 14
Do, 10.03.2022, 14:15 - 18:00 Uhr, HS 14
Do, 17.03.2022, 14:15 - 18:00 Uhr, HS 14
Do, 24.03.2022, 14:15 - 18:00 Uhr, HS 14
Do, 31.03.2022, 14:15 - 18:00 Uhr, HS 14
Do, 07.04.2022, 14:15 - 18:00 Uhr, HS 14 |
| Umfang |
2 Semesterwochenstunden |
| 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 |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 7 February to 4 March 2022. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course:
https://lms.uzh.ch/url/RepositoryEntry/17168236586 |
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within 7 February - 4 March 2022 is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Individual/group presentation; written paper / 3 Credits
|
| Hörer-/innen |
Ja |
| Kontakt |
ulrich.matter@unisg.ch |
| Literatur |
Walkowiak, Simon (2016): Big Data Analytics with R. Birmingham, UK: Packt Publishing.
Wickham, Hadley (2019): Advanced R. Second Edition, CRC Press, FL: Boca Raton.
Wickham, Hadley and Dianne Cook and Heike Hofmann (2015): Visualizing statistical models: Removing the blindfold.
Statistical Analysis and Data Mining: The ASA Data Science Journal. 8(4):203-225.
Schwabish, Jonathan A. (2014): An Economist's Guide to Visualizing Data. Journal of Economic Perspectives. 28(1):209-234.
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