Data science for complex systems

Part I. Introduction: Facets of complex systems; Part II. Heterogeneity and dependence: Quantifying heterogeneity: classical and Bayesian statistics; Statistical analyses of time-varying phenomena; Part III. Patterns and interlinkages: Pattern recognition in complex systems: machine learning; Interlinkages and heterogeneity: network theory; Part IV. Emergence: from micro to macro: Interaction and emergence:agent-based models.