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57366-01 - Colloquium: Methods for Unstructured Data (3 CP)
| Semester | fall semester 2022 |
| Course frequency | Irregular |
| Lecturers | Helge Liebert (helge.liebert@unibas.ch, Assessor) |
| Content | 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 |
| Learning objectives | 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. |
| Bibliography | 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 |
| Course application | 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. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 12.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
Dates
| Date | Time | Room |
|---|---|---|
| Tuesday 11.10.2022 | 08.30-12.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Wednesday 19.10.2022 | 08.15-12.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 20.10.2022 | 12.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Wednesday 26.10.2022 | 08.15-12.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 27.10.2022 | 12.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 03.11.2022 | 12.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Modules |
Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics) |
| Assessment format | record of achievement |
| Assessment details | Take-home assignment. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |