| Nr. | LV-Typ(en) | LV-Titel | |
| 5593 | PI | Applications of Data Science 
Anmeldung über LPIS vom 02.02.2024 14:00 bis 11.02.2024 23:59 Abmeldung über LPIS vom 02.02.2024 14:00 bis 03.03.2024 23:59 |
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| LV-Leiter/in | Sri Harikrishnan, M.Sc., Jorao Gomes Junior, MSc., Univ.Prof. Dr. Verena Dorner |
| Planpunkte Bachelor | SBWL Kurs IV - Data Science Course IV - Data Science Kurs IV - Data Science |
| Semesterstunden | 2 |
| Unterrichtssprache | Englisch |
| Termine | ||||
| Mi, | 06.03.2024 | 16:00-20:00 Uhr | D2.0.342 Teacher Training Raum (Lageplan) | |
| Mi, | 13.03.2024 | 16:00-20:00 Uhr | D2.0.342 Teacher Training Raum (Lageplan) | |
| Mi, | 20.03.2024 | 16:00-20:00 Uhr | D2.0.342 Teacher Training Raum (Lageplan) | |
| Mi, | 10.04.2024 | 16:00-20:00 Uhr | D2.0.342 Teacher Training Raum (Lageplan) | |
| Mi, | 17.04.2024 | 16:00-20:00 Uhr | D1.1.078 (Lageplan) | |
| Mi, | 24.04.2024 | 17:00-20:00 Uhr | TC.4.03 (Lageplan) | |
| Termindownload (ical) | Termine abonnieren | ||||
| Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5593 |
| Kontakt: | ||
| sri.harikrishnan@wu.ac.at | ||
| Inhalte der LV: | ||
This course aims to explore fundamental topics of recommendation systems. Participants will explore the different types of recommendation systems that are found on e-commerce websites. In particular, we will discuss the assumptions of varying recommendation systems regarding available data, user preferences, decision processes, and environments and get familiar with appropriate data modeling and analysis methods. Students will also learn to evaluate and adapt recommendation systems to fit various requirements. The programming language to be used in the course is either Python or R. |
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| Lernergebnisse (Learning Outcomes): | ||
After completing this course, students will have an understanding of the different types of recommendation systems. Students will have learned how they fit other environmental and user parameters. They will use statistical tools to implement recommendation systems, analyze relevant data, and interpret the data. This course aims to contribute to students’ ability to translate decision situations into design parameters of a decision support tool, implement and evaluate such tools, and present their results in an academically appropriate and engaging fashion. |
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| Regelung zur Anwesenheit: | ||
The rules on the attendance of a Continuous Assessment Course (PI) apply. 80% attendance is mandatory. See the dedicated page on the WU portal for further information. |
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| Lehr-/Lerndesign: | ||
Each course session will consist of a lecture introducing a specific topic and methodology and a hands-on programming lab, implementing the lecture's main ideas in code. |
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| Leistung(en) für eine Beurteilung: | ||
The final grade will be composed of: - presentations on group assignment (35%) - a final report (including code and documentation) on group assignment (40%) - in-class assignments and active participation (25%)
Grading Scale: unsatisfactory: ≤ 50 % sufficient: > 50 % to ≤ 62.5 % satisfactory: > 62.5 % to ≤ 75 % good: > 75 % to ≤ 87.5 % excellent: > 87.5 % |
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| Teilnahmevoraussetzung(en): | ||
A successful conclusion of course 1 of SBWL Data Science. Please be aware that, for all courses in this SBWL, registration is only possible for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science). |
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