96342 - BIG DATA IN SOCIAL SCIENCES

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

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:

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:

1. Group or individual project/assignement (max 3 people). Instructions on the project will be distributed in class. (30% of the final grade)
2. Midterm (30% of the final grade)
3. 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