- Docente: Nicola Barban
- Credits: 8
- SSD: SECS-S/04
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: First cycle degree programme (L) in Economics, Politics and Social Sciences (cod. 5819)
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from Sep 18, 2023 to Dec 12, 2023
Learning outcomes
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.
Course contents
- Causality
- Measurement
- Reducing Data Complexity
- Prediction
- Data Visualization
- Probability
- Uncertainty
Readings/Bibliography
Course textbook:
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 Sciences: An Introduction. Princeton University Press
Additional resources:
R for Data Science, 2nd Edition by Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund
available entirely online here:
Teaching methods
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
Assessment methods
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.
Teaching tools
- RStudio
- R Markdown
Office hours
See the website of Nicola Barban