An introduction to network analysis: representing relationships such as alliances, trade flows and collaborations as networks, measuring centrality, detecting communities, testing whether similar actors connect, and identifying core–periphery structures, with daily hands-on labs in R. No prior experience needed.
An overview of machine learning and pattern recognition: data exploration and preparation, classification and regression, association rules and clustering, with applications in Python.
An introduction to network analysis and graph theory, network structure and centrality measures, and community detection and assortativity, with hands-on sessions in R and igraph each day, followed by one-to-one sessions for additional help.
Causal inference (randomized experiments, instrumental variables, regression discontinuity) and network analysis (centrality, communities, core–periphery structure), with applications in research and business using R.
The course is an introduction to the economic analysis through the study of elementary microeconomic principles. Consumer decisions and firm choices are presented and discussed to understand market based systems and, particularly, price formation and efficiency.