B0278 - BIG DATA FOR PEACE STUDIES

Contenuti

Il corso sarà tenuto da altro docente il cui nominativo sarà indicato a breve.

 

This course will teach students about the role of big data in modern life, as well as its uses as a tool for good or evil. Students will learn about how big data can help us to understand and explain social phenomena in a way that was unthinkable in previous generations. Throughout the course, we will apply the R statistical computing environment to large-scale data sets, explore packages designed for use with big data (such as data.table and ff), and learn how parallelization can be used to analyze lots of data quickly.

Testi/Bibliografia

The required readings for each class are listed on the syllabus, below the topic to be covered. Students are expected to do the reading before coming to class. Our primary textbook for most of

the class will be the following, denoted “Foster” below:

Foster, Ian, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane. 2017. Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: Taylor and Francis Group, Boca Raton.

When we deal with applications within R, we will use the following text, which will be denoted “Walkowiak”:

Walkowiak, Simon. 2016. Big Data Analytics with R. Birmingham, UK: Packt Publishing, Ltd. (ISBN: 978-1786466457)

Finally, Wickham and Grolemund’s text is not required, but is recommended:

Wickham, Hadley and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visu- alize, and Model Data. Sebastopol, CA: O’Reilly Media, Inc. (ISBN: 978-1491910399)

Other readings will come from relevant articles, which students will be able to access.

Metodi didattici

Lezioni e seminari

Modalità di verifica e valutazione dell'apprendimento

Student assessment will come from three sources:

• Final Exam (40%): at the end of the course, students will take a cumulative final exam that tests their knowledge of statistics, programming, and the ethics and particular challenges of big data, as applied to peace studies.

• Research Project (40%): students will choose a big data research project, which they will work on in phases throughout the class. The final session will be given over to class presentations, in which students will discuss how they obtained, cleaned, and analyzed their data, along with any ethical concerns that they had to address. Students will email the final project—a short paper, summarizing the data collection and analysis, in PDF format—to the instructor. This is not meant to be a full research paper, but rather an exploration of a large data set.

Homework and class participation (20%): students are expected to attend class and participate regularly. In addition, the instructor will assign short problem sets periodically throughout the class

Strumenti a supporto della didattica

Slides, other relevant material will be provided in class.

Orario di ricevimento

Consulta il sito web di John Tyson Chatagnier