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Root number
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420844 |
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Semester
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HS2026 |
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Type of course
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Lecture |
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Allocation to subject
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Business Administration |
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Type of exam
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Written exam |
| Title |
Data Visualization and Machine Learning |
| Description |
In the first part of the course, the students will develop the skills to analyze, understand, and communicate data using effective visualization techniques. In the second part of the course, the students will learn the fundamental concepts and techniques of machine learning, including supervised and unsupervised learning, and how to apply these techniques to real-world problems. A previous attendance of the courses "Programming for data scientists I" and "Programming for data scientists II" is recommended but not mandatory. Note: The course software is Python, and the students will need to bring their own device to class.
Please note that the course was previously called Business Analytics. If you have attended Business Analytics, you cannot take this course for credit. |
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ILIAS-Link (Learning resource for course)
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Registrations are transmitted from CTS to ILIAS (no admission in ILIAS possible).
ILIAS
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Link to another web site
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| Lecturers |
Prof. Dr.
Philipp Baumann, Institute of Financial Management - Division for Quantitative Methods in Business Administration ✉
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Maude Bersier, Institute of Financial Management - Division for Quantitative Methods in Business Administration ✉
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ECTS
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3 |
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Recognition as optional course possible
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Yes |
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Grading
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1 to 6 |
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| Dates |
Thursday 10:15-12:00 Weekly
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Thursday 17/12/2026 12:15-13:15
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Wednesday 17/2/2027 11:15-12:15
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| Rooms |
Hörraum 101, Hauptgebäude H4
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| External rooms |
Hörraum 205, Hauptgebäude H4
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Hörsaal S 003, UniS
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| Students please consult the detailed view for complete information on dates, rooms and planned podcasts. |
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