Data Science

Catalog description

Introduces the data analytics pipeline relevant to graduate research work: obtaining raw unstructured data; cleaning, organizing, merging and identifying potential pitfalls in the data; exploring and visualizing the underlying statistics; introduction to preliminary stochastic, generative and econometric modeling methods. Introduces best-practices for handling and analyzing large multi-scale datasets using examples drawn from open-data repositories.