Introduction to R for Data Science & Computational Social Science

Dozent/in	Fabian Mundt, M.A.
Veranstaltungsart	Masterseminar
Code	HS231651
Semester	Herbstsemester 2023
Durchführender Fachbereich	NULL_MA_LUMACSS
Studienstufe	Master
Termin/e	Fr, 29.09.2023, 09:15 - 17:00 Uhr, HS 12 Sa, 30.09.2023, 09:15 - 17:00 Uhr, HS 12 Fr, 03.11.2023, 09:15 - 17:00 Uhr, HS 12 Sa, 04.11.2023, 09:15 - 17:00 Uhr, HS 12
Inhalt	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.
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: priority for LUMACSS students. In case of too many registrations by other disciplines, a draw will be made to decide who may remain in the course.
Sprache	Englisch
Begrenzung	priority for LUMACSS students
Abschlussform / Credits	Aktive Teilnahme mit abschliessendem Beispielprojekt / 4 Credits
Kontakt	mundt@ph-karlsruhe.de
Literatur	· Wickham and Grolemund (2017). 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