| Dozent/in |
Dr. Fabian Mundt |
| Veranstaltungsart |
Masterseminar |
| Code |
HS261661 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
NULL_MA_LUMACSS |
| Studienstufe |
Master |
| Termin/e |
Fr, 25.09.2026, 09:15 - 17:00 Uhr, 2.A10
Sa, 26.09.2026, 09:15 - 15:30 Uhr, 3.B57
Fr, 16.10.2026, 09:15 - 17:00 Uhr, 3.B52
Sa, 17.10.2026, 09:15 - 15:30 Uhr, 3.B52 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
Blockseminar |
| Inhalt |
NOTE: Please register for this course by 15th September 2026 on the UniPortal. After that, if too many students have registered, a draw will be made, and you will get notified whether you can attend the course on 16th September 2026. LUMACSS students are prioritized as this course is mandatory for them.
The »Introduction to R for Data Analytics and Computational Social Science« offers a complete introduction to the R programming language for data science and computational social science. Taking this course students will learn how to:
- Setting up and handling the RStudio IDE.
- Import, transform, visualize, and model data;
- Recognize and handle common data structures;
- Setup a reproducible data science project;
- Communicate effectively a project’s insights;
- Automating common tasks to minimize errors and time loss.
Data literacy is increasingly required in business, technology, and academic work because data is everywhere. R is the lingua franca of data science for its powerful and easy-to-use tools for statistical analysis and data visualization.
This course is designed for master’s students specializing in a quantitative-oriented or computational social science program. No prior experience or knowledge in data analysis and programming is required. However, students must be curious and animated by an intrinsic motivation to learn R and data science. The teaching style is hands-on and participative: instructive phases, which usually consist of live coding, alternate with individual phases that enable the participants to deepen their learning and insights. A passive »sit and listen« attitude is discouraged since this typically undermines effective and durable learning.
Both Base R approaches and modern variants of the Tidyverse will be discussed and practiced during the two block sessions. In addition to the learning and experimental application, the focus will be on the creation of a (blueprint) project that depicts the data science workflow and creates a practical and realistic setting based on real research data and problems.
|
| E-Learning |
https://lms.uzh.ch/url/RepositoryEntry/17589469281 |
| Lernziele |
Data literacy (import, preprocessing, analytics); data independence; basic R programming; workflow reproducibility. |
| Voraussetzungen |
An intrinsic motivation to learn R and data science is the only requirement for this course.
Limitation for participants: priority for LUMACSS students. In case of too many registrations, a draw will be made to decide who may remain in the course. Therefore, registration on the UniPortal before this date is essential. |
| Sprache |
Englisch |
| Begrenzung |
priority for LUMACSS students |
| Leistungsnachweis |
Aktive Teilnahme mit abschliessendem Beispielprojekt / 4 Credits |
| Abschlussform / Credits |
Aktive Teilnahme mit abschliessendem Beispielprojekt / 4 Credits
|
| Hinweise |
NOTE: Please register for this course by 15th September 2026 on the UniPortal. After that, if too many students have registered, a draw will be made, and you will get notified whether you can attend the course on 16th September 2026. LUMACSS students are prioritized as this course is mandatory for them. |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
mundt@ph-karlsruhe.de |
| Literatur |
- Wickham and Grolemund (2023). R for Data Science. O’Reilly. https://r4ds.had.co.nz
- Locke (2017). Working with R. Locke Data· Locke (2017). Data Manipulation in R. Locke Data· Wickham (2019). Advanced R. CRC |