Vorlesungsverzeichnis

Suchen Sie hier über ein Suchformular im Vorlesungsverzeichnis der Leuphana.


Lehrveranstaltungen

Methoden-Training – ein Vorbereitungskurs in der quantitativen Forschung (Seminar)

Dozent/in: Oliver Hormann

Termin:
14-täglich | Donnerstag | 15:00 - 18:00 | 27.10.2022 - 02.02.2023 | C 40.165 | C 40.165

Inhalt: Die Veranstaltung bietet eine grundlegende Auseinandersetzung mit den methodologischen und methodischen Grundlagen der quantitativen Forschung sowie eine SPSS-basierte Einführung in multivariate Analysetechniken (varianz- und regressionsanalytische Verfahren). Der Kurs findet in 7 Blöcken über das Semester verteilt statt und dient der promotionsspezifischen Vorbereitung auf die empirische Bearbeitung einer quantitativen Fragestellung.

Quantitative Data Analysis in R: The Tidyverse Approach (Seminar)

Dozent/in: Jan-Bennet Voltmer, Katharina Voltmer

Termin:
Einzeltermin | Fr, 28.10.2022, 14:15 - Fr, 28.10.2022, 19:45 | Online-Veranstaltung | Online
Einzeltermin | Fr, 04.11.2022, 14:15 - Fr, 04.11.2022, 19:45 | C 11.320 | .
Einzeltermin | Sa, 05.11.2022, 10:15 - Sa, 05.11.2022, 19:45 | C 11.307 | .

Inhalt: Why not Python? "People have divided the Data Science field into camps based on the choice of the programming language they use. There is an R camp and a Python camp and history is a testimony to the fact that camps cannot live in harmony. Members of both the camps fervently believe that their choice of language is superior to the other. So, in a way, divergence doesn’t lie with the tools but with the people using those tools." (Pandey, 2019) "R is developed by academics and scientist. It is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries. [...] After you know your first programming language, learning the second one is simpler." Therefore, participants will practice the programming language R using prepared syntax. Participants will work on small programming tasks on their own and in small groups to practice and deepen their statistics and R skills. In particular, the seminar will focus on the relatively new but very intuitive “tidyverse” approach of R programming. This consists of some very easily relatable “verbs” (i.e., select, filter, mutate, group_by, summarize), to ease accessibility of R analyses.