DSC 259RPractice and Applications
In-depth study of the data science lifecycle in practice including building large-scale, working systems on real-world data; methods for properly collecting and cleaning data, building and testing models; dealing with messy data, hypothesis and permutation testing, missing data and imputation, machine learning, model evaluation and communication. Addressing challenging problems using data science algorithms and modifying applications of data science in practice. This is a distance education course.