Introduction to R for Data Science & Computational Social Science

Dozent/in	Dr. Andrea De Angelis
Veranstaltungsart	Masterseminar
Code	HS221516
Semester	Herbstsemester 2022
Durchführender Fachbereich	Politikwissenschaft
Studienstufe	Master
Termin/e	Fr, 30.09.2022, 09:15 - 17:00 Uhr, 3.B55 (Terminierung 1) Sa, 01.10.2022, 09:15 - 17:00 Uhr, 3.B55 (Termine) Fr, 21.10.2022, 09:15 - 17:00 Uhr, HS 12 (Termine) Sa, 22.10.2022, 09:15 - 17:00 Uhr, 3.B52 (Termine)
Umfang	2 Semesterwochenstunden
Turnus	Blockveranstaltung
Inhalt	Content: 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: 1. Import, transform, visualize, and model data; 2. Recognize and handle common data structures; 3. Set up a reproducible data science project; 4. Communicate effectively a project’s insights; 5. Program 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: class activities include a range of interactive active-learning tools, from pair-programming to live-coding sessions and quizzes. A passive “sit and listen” attitude is discouraged since this typically undermines effective and durable learning. This five-day course introduces the participants to all the key elements and packages of the modern implementation of the R language (known as ‘tidyverse’): Manipulating data with dplyr and tidyr. Reporting data analysis with rmarkdown. Statistical modelling and elements of programming. Practicing it all in a capstone project. In the morning session (9:15 – 13:00) the instructor introduces the topic of the day using examples, guided exercises and class. In the afternoon session (14:15 – 16:45) the participants apply what they learned in the morning with exercises and live-coding sessions.
Lernziele	Data literacy (import, preprocessing, analytics); data independece; basic R programming; workflow reproducibility.
Voraussetzungen	An intrinsic motivation to learn R and data science is the only requirement for this course.
Sprache	Englisch
Begrenzung	priority for LUMACSS students
Leistungsnachweis	No exam / 4 ECTS
Abschlussform / Credits	Abgabe von Aufgaben / 4 Credits
Hörer-/innen	Nein
Kontakt	andrea.deangelis@unilu.ch
Literatur	· Imai (2017). Quantitative Social Science: an Introduction: Princeton: Princeton University Press. · Wickham and Grolemund (2017). R for Data Science. O’Reilly.