- Docente: Marco Berrettini
- Crediti formativi: 2
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea in Economics, Politics and Social Sciences (cod. 5819)
-
dal 21/09/2023 al 07/12/2023
Conoscenze e abilità da conseguire
Students develop transversal skills with a focus on the development of skills complementary to the quantitative methods’ courses. In particular, students acquire skills in data analysis and in the use of dedicated software and programming languages such as R or Python, as well as skills in data visualization.
Contenuti
R programming:
- user-written functions
- loops
R package tidyverse:
- data manipulation (subsetting, merging, summarising) with dplyr
- data visualization with ggplot2
- web scraping with rvest
R Markdown
Testi/Bibliografia
Suggested readings:
Further readings:
- Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science. O'Reilly.
- Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
- Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The definitive guide. CRC Press.
Metodi didattici
Computer laboratory sessions.
In consideration of the type of activity and the teaching methods adopted, the attendance of this training activity requires the prior participation of all students in the training modules 1 and 2 on safety in the study places, in e-learning mode. (https://elearning-sicurezza.unibo.it/)
Modalità di verifica e valutazione dell'apprendimento
Pass/fail exam consisting in a take-home project; group work (max. 4 people) is allowed, accompanied by precise indications about the division of tasks.
The exam aims at evaluating the acquired skills in the use of R for data analysis and visualization.
Although attending classes is not mandatory, it is strongly recommended.
Strumenti a supporto della didattica
- R scripts
- Datasets
- Slides
Orario di ricevimento
Consulta il sito web di Marco Berrettini