Universität Wien

040514 UE Python for Finance II (MA) (2024S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 07.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 14.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 21.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 28.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 04.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 11.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 18.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Tuesday 25.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Aims, contents and method of the course

The course enables participants to gain further experience in Python and its applications in Finance. It is expected that students have prior knowledge of Python equivalent to the contents of Python for Finance I. Participants get to know and apply methods from machine learning and natural language processing, with a focus on practical applications of these methods. Students learn to gather textual data from different internet sources, clean the data, and process the data to produce quantitative measures that might be relevant for a task (e.g. an investment strategy). Subsequently, this data and other standard finance data sources are used as inputs to machine learning methods. Besides these specific methods, students will also gain further general knowledge with respect to Python programming and managing a programming project.

Assessment and permitted materials

The grade will be based on homework exercises that participants are expected to present in class, class participation, and a course project in which students apply the methods learnt in the course.

Minimum requirements and assessment criteria

60% homework exercises
10% class participation
30% course project

Minimum requirement for a positive grade: a total of 50%.

Examination topics

All material covered in class.

Reading list

Bird, S., Klein, E., Loper, E., Natural Language Processing with Python, 2019. https://www.nltk.org/book/

Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st (2017), 2nd (2019), or 3rd (2022) edition. O'Reilly Media.
or
Géron, A., Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. 2nd edition (2020). O'Reilly Media.

Raschka, S., Mirjalili, V., Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd (2017) or 3rd (2019) edition. Packt Publishing.
or
Raschka, S., Mirjalili, V., Machine Learning mit Python und Scikit-Learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning, 2. Auflage, 2018, or 3. Auflage, 2021. MITP.
or
Raschka, S., Mirjalili, V., Liu, Y. (H.), Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. 1st edition (2022). Packt Publishing.

Association in the course directory

Last modified: We 31.07.2024 11:25