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| Semester | Herbstsemester 2022 |
| Angebotsmuster | unregelmässig |
| Dozierende | Helge Liebert (helge.liebert@unibas.ch, BeurteilerIn) |
| Inhalt | Much of human knowledge is stored in unstructured formats. This course teaches methods to process and analyze unstructured data, focusing on text data. In the first part, we review tools required for processing text data. One lecture will be dedicated to web scraping fundamentals. We then focus on the concepts underlying the transformation of unstructured data into structured formats. Finally, we study supervised models suited for the analysis of text data, as well as unsupervised models which make it possible to discover structure in unlabeled text data. Throughout the course, I will emphasize real-world applications of the techniques in research and industry. Course outline 1. PC fundamentals 2. Regular expressions and pattern recognition. 3. Web scraping 4. Representing text as data 5. Analysis of text data: Supervised models 6. Analysis of text data: Unsupervised models |
| Lernziele | The course aims to provide a thorough understanding of the workflow, tools and models related to the analysis of text data, and their implementation in R. |
| Literatur | The course does not adhere strictly to a single reference. References are pointed out in the course material. The two books below serve as a general reference. Jurafsky, D. and Martin, J. H. (2019). Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/. Hastie, T., Tibshirani, R. and Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer, New York. https://web.stanford.edu/~hastie/ElemStatLearn/. |
| Weblink | Course website |
| Anmeldung zur Lehrveranstaltung | Registration: Please enrol in the Online Services. EUCOR-Students and students of other Swiss Universities have to enrol at the students administration office (studseksupport1@unibas.ch) within the official enrolment period. In order to get access to ADAM in time, it is best to enrol before the course starts though. Enrolment = Registration for the exam! If you have any questions, please do not hesitate to contact the Graduate School administration at gsbe-wwz@unibas.ch. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Donnerstag | 12.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Datum | Zeit | Raum |
|---|---|---|
| Dienstag 11.10.2022 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Mittwoch 19.10.2022 | 08.15-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Donnerstag 20.10.2022 | 12.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Mittwoch 26.10.2022 | 08.15-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Donnerstag 27.10.2022 | 12.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Donnerstag 03.11.2022 | 12.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Module |
Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät) |
| Leistungsüberprüfung | Leistungsnachweis |
| Hinweise zur Leistungsüberprüfung | Take-home assignment. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |