312952 Introduction to Natural Language Processing with Python (S) (WiSe 2024/2025)

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This course provides a hands-on introduction to Natural Language Processing for PhD students who want to analyze text in their research. It is designed for beginners with no prior knowledge of Python.

The course will be held on the following six days, from 10:15-11:45 a.m. (room V8.240):

1. 29.10. (Tuesday)
2. 31.10. (Thursday)
3. 5.11. (Tuesday)
4. 7.11. (Thursday)
5. 12.11. (Tuesday)
6. 14.11. (Thursday)

More details on the content of the course can be found in the Lernraum.

Requirements for participation, required level

Prerequisites and application

The course is intended for beginners without prior Python knowledge. However, to ensure a level playing field, you are asked to solve one basic task prior to the course, following five steps:

1. Install Python on your computer, following one of the many step-by-step online tutorials. I suggest installing Miniconda to manage your packages. As a code editor, I like to use Microsoft Visual Studio Code. Moreover, I suggest creating a virtual environment specifically for this course. You can find more information for example on this website: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html

2. Import some basic Python libraries: Pandas, Numpy and Matplotlib. You may need to import more later on.

3. Import a data file of your choice (e.g., csv or excel) using the Pandas library.

4. Create a visualisation of your choice using the Matplotlib library. The title of the plot should include the name of this course ("NLP for beginners").

5. Save your solution as a notebook (.ipynb) and submit it via email until October 27, 2024 (12 p.m.).

Teaching staff

Dates ( Calendar view )

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Subject assignments

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Economics and Management (BiGSEM) / Promotion Economics; Electives   2  
Economics and Management (BiGSEM) / Promotion Management; Electives   2  
Economics and Management (BiGSEM) / Promotion Finance; Electives   2  
Studieren ab 50    

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E-Learning Space
E-Learning Space
Address:
WS2024_312952@ekvv.uni-bielefeld.de
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If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_473610936@ekvv.uni-bielefeld.de
Notes:
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Last update basic details/teaching staff:
Monday, May 13, 2024 
Last update times:
Monday, May 13, 2024 
Last update rooms:
Monday, May 13, 2024 
Type(s) / SWS (hours per week per semester)
seminar (S) / 1
Department
Faculty of Business Administration and Economics
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473610936