Nr. | LV-Typ(en) | LV-Titel | |
4971 | FS | Data Science Lab
Anmeldung über LPIS vom 01.02.2024 15:00 bis 11.02.2024 23:59 Abmeldung über LPIS vom 01.02.2024 15:00 bis 11.03.2024 23:59 |
LV-Leiter/in | PD Dr. Ronald Hochreiter, Univ.Prof. Dr. Axel Polleres |
Planpunkte Bachelor | SBWL Kurs V - Data Science Course V - Data Science Kurs V - Data Science |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Do, | 14.03.2024 | 10:00-14:00 Uhr | TC.2.02 (Lageplan) | |
Do, | 25.04.2024 | 11:30-19:30 Uhr | TC.5.05 (Lageplan) | |
Fr, | 26.04.2024 | 10:00-18:00 Uhr | D4.0.022 (Lageplan) | |
Do, | 20.06.2024 | 10:00-18:00 Uhr | TC.1.02 (Lageplan) | |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/4971 |
Kontakt: | ||
axel.polleres@wu.ac.at, ronald.hochreiter@wu.ac.at | ||
Inhalte der LV: | ||
The final course of the SBWL Data Science will be conducted in group projects that are introduced in a joint kickoff-workshop together with"Data Coaches" (members of one of the involved institutes and from industry partners). Thereafter, the project teams are formed and each team will have to elaborated, together with their data coach, a concrete project plan for a Data Science project to be conducted over the duration of the semester, involving regular interactions with the data coach and the teachers of the course. It id the objective of this course to develop a front-to-end solution proposal to a practical problem in a team. The data coaches will provide data sets and tools from realworld use cases (from industry or from open data). The coordination will be done in 2 parallel courses, each of which takes over supervision of half of the teams. Each team will consist of 3-4 students. |
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Lernergebnisse (Learning Outcomes): | ||
You will learn the following in this course:
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Regelung zur Anwesenheit: | ||
Attendance of the plenary introduction session, the intermediate meetings (for individual groups) and the final plenary presentations (all students are expected to be present and give feedback to all the others' presentations) is required. In addition, at least two sparing group meetings - one between introduction and intermediate as well as one between intermediate and final - are mandatory. |
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Lehr-/Lerndesign: | ||
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Leistung(en) für eine Beurteilung: | ||
We will assess the following partial contribtions for grading the course: |
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Teilnahmevoraussetzung(en): | ||
Successful conclusion of course 1+2 of SBWL Data Science. Please be aware that for all courses in this SBWL registration is only possibly for students who successfully have completed the entry exam or admission (Access to the SBWL: Data Science). Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only. |
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