Nr. | LV-Typ(en) | LV-Titel | |
5038 | PI | Advanced Data Analysis with R
Anmeldung über LPIS vom 19.02.2024 15:00 bis 01.03.2024 23:59 |
LV-Leiter/in | Dr. Marcus Wurzer |
Planpunkte Doktorat/PhD | Vertiefung in den Forschungsmethoden der Sozial- und Wirtschaftswissenschaften Forschungsmethoden Vertiefung in den Forschungsmethoden der Sozial- und Wirtschaftswissenschaften |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Mo, | 04.03.2024 | 12:45-14:15 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 11.03.2024 | 12:45-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 18.03.2024 | 12:45-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 08.04.2024 | 12:45-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 15.04.2024 | 13:15-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 22.04.2024 | 13:15-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 29.04.2024 | 13:15-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 06.05.2024 | 13:15-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 13.05.2024 | 13:15-15:15 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 27.05.2024 | 13:15-15:15 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 10.06.2024 | 12:45-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 17.06.2024 | 12:45-14:45 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Mo, | 24.06.2024 | 12:45-15:15 Uhr | D2.0.025 Workstation-Raum (Lageplan) | |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5038 |
Kontakt: | ||
marcus.wurzer@wu.ac.at | ||
Inhalte der LV: | ||
R is a high-level language and an environment for data analysis and data visualization. While many important statistical methods are already included in the base R installation, the main benefit is its open-source philosophy which makes R highly extensible and renders possible the availability of new, cutting edge applications in many different fields. The popularity of R increased constantly during the last years and by now, it is arguably the most popular software for data analysis in the statistical community. The course starts with an standard part that focuses on the following:
Depending upon students' interests and the data sets they want to analyze, a selection of these additional methods may be covered:
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Lernergebnisse (Learning Outcomes): | ||
Upon completion of the course students are able to:
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Regelung zur Anwesenheit: | ||
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Lehr-/Lerndesign: | ||
Lectures, Practicals |
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Leistung(en) für eine Beurteilung: | ||
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