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CX202: Complex Systems Modeling, Networks, and Data Analytics
This course provides (a) an expanded introduction to the fundamentals of data analytics, (b) an introduction to building models of complex systems (physical, biological, social and engineered), and (c) the study of networks, including topologies and dynamics of real world networks.
The data analytics lessons will cover skills needed to transform raw data into visualizations and insight. A variety of visualization techniques will be covered, including interactive representations. Analytic methods to be covered include: time series analysis, network analysis, data mining, machine learning, distribution fitting, and more. Students will learn to obtain and prepare data for analysis. An overview of academy- and industry-standard toolboxes for handling data will be given, including the construction of databases, visualization, and analysis.
The course will cover the basic construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer program. Particular attention will be paid to choosing the right level of detail for the model, testing its robustness, and discussing which questions a given model can or cannot answer.
The study of networks will introduce the use of network topologies and the characterization of networks describing complex systems, including such concepts as small worlds, degree distribution, diameter, clustering coefficient, modules, and motifs. Different types of network topologies and network behaviors that model aspects of real complex systems will be described including: modular, sparse, random, scale-free, influence, transport, transformation, and structure.