- Docente: Nicola Barban
- Crediti formativi: 8
- SSD: SECS-S/04
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea in Economics, Politics and Social Sciences (cod. 5819)
-
dal 16/09/2024 al 13/12/2024
Conoscenze e abilità da conseguire
The course provides a bridge between statistics, computer science and the social sciences. By the end of the course students gain a basic knowledge of the main multivariate statistical methods used in the field of Big Data and the knowledge to carry them out for addressing critical research questions in the social science field. Real-world problem concerning social phenomena will be presented and analyzed through updated statistical methods and tools using R.
Contenuti
- Causality
- Measurement
- Reducing Data Complexity
- Prediction
- Data Visualization
- Probability
- Uncertainty
Prerequisites: Students should have a basic knowledge of the Rprogramming language and an understanding of linear regression models. These prerequisites are essential for effectively engaging with the course content and maximizing the benefits of the advanced topics discussed.
Testi/Bibliografia
Course textbook:
Kosuke Imai and Nora Webb Williams. Quantitative Social Science: An Introduction in tidyverse. Princeton University Press ISBN:9780691222271
Note: This is the preferred version that focuses on tidyverse. In alternative the following version of the book is also good.
Kosuke Imai Quantitative Social Science: An Introduction: Princeton University Press
Additional resources:
Bit By Bit: Social Research in the Digital Age Princeton University Press
R for Data Science, 2nd Edition by Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund available entirely online here:
Metodi didattici
The teaching structure will be composed by frontal lectures and lab sessions using the software R. The course will host guest lectures from expert in data analysis from academia and the industry
Modalità di verifica e valutazione dell'apprendimento
For students attending class regularly, the final evaluation will be composed by three parts:
- Group or individual project/assignement (max 3 people). Instructions on the project will be distributed in class. (30% of the final grade)
- Midterm (30% of the final grade)
- Final exam (40% of the final grade)
Students not attending class are invited to contact the instructor to discuss the examination.
Strumenti a supporto della didattica
- RStudio
- R Markdown
Orario di ricevimento
Consulta il sito web di Nicola Barban