Data and Visual Analytics

Catalog description

Introduces students to high performance computing and statistical techniques used for visualizing high-dimensional and complex data. The course emphasizes how high performance parallel computing with CPUs and GPUs can be used for data analytics tasks, such as data cleaning and mining, dimension reduction and regularization, clustering, graph algorithms, decision trees, random forests, hierarchical models, and fitting other statistical and mathematical structures to both effectively visualize and analyze data. The course emphasizes how to utilize software to exploit the synergies between computation and visualization. Students work in small groups to complete a team project, which requires a poster presentation and final technical report. Prerequisite(s): DATS 7510 >= C Lecture Hours: 3 Repeatability: May not be repeated for credit. Grade Mode: Normal, Audit Program Restrictions: DPHIL_BIOS, MS_BIOS, MS_DSCI Campus Restrictions: AU Online Schedule Type (Primary): Lecture Schedule Type (Additional): Asynchronous Instruction Click here for the Schedule of Classes.