B0278 - BIG DATA FOR PEACE STUDIES

Learning outcomes

This course provides an introduction to the concept of big data analysis, with specific applications to the social sciences. In broad terms, it aims to equip students with an understanding of the role of big data in modern cultural and academic life. This includes examination of the ethical and scientific pitfalls associated with the use of such data. By the end of the course, students will be able to: (1) formulate questions that can be answered using big data applications; (2) use common tools for downloading and working with large data sets; (3) understand and comply with existing best practices for data analysis; (4) communicate techniques and results to both experts and lay people; and (5) understand text mining techniques and how to use text as data.

Course contents

The course will be held by a different instructor, whose name will be communicated soon.

 

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

Readings/Bibliography

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.

Teaching methods

Lectures and seminars

Assessment methods

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

Teaching tools

Slides, other relevant material will be provided in class.

Office hours

See the website of John Tyson Chatagnier