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| Semester | fall semester 2020 |
| 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 | Please enrol by email to Graduate School of Business and Economics <gsbe-wwz@unibas.ch> until September 14, 2020. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
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No dates available. Please contact the lecturer.
| Modules |
Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics) |
| Assessment format | end-of-semester examination |
| Assessment details | Take-home assignment. |
| Assessment registration/deregistration | Registration/deregistration: teaching staff |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |