Advanced Social Data Science I

After completing the course, the student is expected to be able to:

Knowledge:

• Show familiarity with advanced regression methods and different research designs for casual inference in the social sciences
• Describe cores concepts and methods in supervised machine learning, including linear models, tree-based classification, overfitting, bias/variance trade-off and cross-validation.
• Be able to define empirical issues at the intersection between machine learning and social science and describe challenges of interpretability of machine learning models.
• Define key concepts in the analysis of complex networks.

Skills:

• Implement common social science identification strategies to handle problems of endogeneity and selection.
• Set up and execute simple supervised machine learning models for measurement and prediction in Python.
• Identify challenges in applying and learning from machine learning in a social science context.
• Structure network data in Python, as well as to construct and extract various network measures.

Competences:

• Design and carry out basic analyses of complex social science networks.
• Evaluate and implement appropriate modelling approaches given data set and objective, i.e. whether the goal is to evaluate a policy, make a model with best fit of the data or construct new measures.
• Critically assess how carious research designs and identification strategies can or cannot be applied to questions of causal relationships in observational and “found” data and use vhis to develop data collection strategies.
• Account for the possibilities and limitations in the use of machine learning within the social sciences and reflect upon contemporary (mis)use of application and machine learning in policy and research contexts.