This course provides an introduction to (a) the study of networks, including topologies and dynamics of real world networks and (b) the fundamentals of data analytics, machine learning, and artificial intelligence.
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.
The data analytics lessons will cover skills needed to transform raw data into visualizations and insight. The course will cover fundamental 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.
Students will learn to obtain and prepare data for analysis. An overview of academy- and industry-standard toolboxes for handling large datasets will be given, including the collection of data using APIs, construction of databases, visualization, and analysis. A variety of visualization techniques will be covered, including interactive representations.
Analytic methods to be covered include: distribution fitting, data mining, machine learning (regression, classification and clustering), network analysis and time series analysis. 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.