Computational Social Sciences Koc University

Overview

Computational social science makes it possible for researchers to collect data from rapidly spreading new data sources such as online platforms, mobile devices, satellites and sensors, and to perform social science analyses with these new types of data. In addition to their direct contribution to social sciences, these new data types and resources are instrumental in the study of topics such as data privacy, artificial intelligence ethics and the spread of false information, thus creating new research areas. Moreover, computational and quantitative methods are increasingly applied in analyses of diverse local and global problems (such as the COVID-19 crisis) and their solution.

This two-year Computational Social Sciences graduate program will provide researchers with the skills to work with larger data sets than they encounter in traditional social science fields, to use state-of-the-art tools such as artificial intelligence and machine learning, text processing, network analysis, etc. and to apply the most up-to-date computational techniques.

Key Features

• As an interdisciplinary program, CSS MA at Koc University aims to create a synergy by combining existing courses and research initiatives with lectures and research techniques on new methods. In this respect, social sciences and humanities courses will enable students to develop their computational and mathematical skills, while students with these skills will deepen their competencies in social science theory and examine social science research questions. The courses and applications aimed at improving the analytical skills of students in the field of social sciences in areas such as big data analysis, computer simulations, network analysis and machine learning constitute the basis of the program content.

• Designed in this way, the program aims to provide new and complementary dimensions to existing graduate programs and to create an important resource in a research area that is much more demanding than the existing human resources can meet.