| Nr. | LV-Typ(en) | LV-Titel | |
| 5893 | PI | Data Science and Artificial Intelligence II 
Die Lehrveranstaltung wird nur im Sommersemester angeboten Anmeldung über LPIS vom 12.02.2024 15:00 bis 15.02.2024 23:59 Abmeldung über LPIS vom 12.02.2024 15:00 bis 30.04.2024 23:59 |
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| LV-Leiter/in | Assist.Prof. PD Dr. Sabrina Kirrane |
| Planpunkte Master | Data Science and Artificial Intelligence II |
| Semesterstunden | 2 |
| Unterrichtssprache | Englisch |
| Termine | ||||
| Fr, | 03.05.2024 | 09:00-13:00 Uhr | D2.0.038 (Lageplan) | |
| Fr, | 17.05.2024 | 09:00-13:00 Uhr | D2.0.038 (Lageplan) | |
| Fr, | 07.06.2024 | 09:00-13:00 Uhr | D2.0.038 (Lageplan) | |
| Fr, | 21.06.2024 | 09:00-13:00 Uhr | D2.0.038 (Lageplan) | |
| Fr, | 28.06.2024 | 09:00-16:00 Uhr | D2.0.038 (Lageplan) | |
| Termindownload (ical) | Termine abonnieren | ||||
| Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5893 |
| Kontakt: | ||
| sabrina.kirrane@wu.ac.at | ||
| Inhalte der LV: | ||
This fast-paced class is intended for students interested business analytics from a data and algorithmic governance perspective. The course focuses on gaining the fundamental knowledge necessary to enable fair, transparent, explainable, and accountable data analytics, with a particular emphasis on the academic, industrial and societal relevancy of the corresponding principles, tools, and technologies. |
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| Lernergebnisse (Learning Outcomes): | ||
Students will understand the principles, tools, and technologies that are necessary to enable fair, transparent, explainable, and accountable data analytics. This includes:
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| Regelung zur Anwesenheit: | ||
According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given. |
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| Lehr-/Lerndesign: | ||
A combination of academic papers and case studies will be used to demonstrate the academic, industrial and societal relevancy of the course content. The applied project will further reinforce knowledge gained in class by affording participants the opportunity to apply their knowledge by critically analysing existing proposals, by discussing challenges faced in practice and by brainstorming about potential solutions. |
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| Leistung(en) für eine Beurteilung: | ||
Class Participation: 10% Project proposal: 20% Applied research project: 70%
Grading Scheme: 90−100 Sehr gut (Really good) is the best possible grade and indicates outstanding performance with no or only minor errors. 80−89 Gut (Good) is the next-highest grade and is given for performance that is above-average standard but with some errors. 64−79 Befriedigend (Satisfactory) indicates generally sound work with a number of notable errors. 51−63 Genügend (Sufficient) is the lowest passing grade and is given if the standard has been met but with a significant number of shortcomings. 0−50 Nicht genügend (Insufficient) is the lowest possible grade and the only failing grade. |
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| Teilnahmevoraussetzung(en): | ||
The participants are expected to have some knowledge of data management and analytics. |
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