ABSTRACT
This chapter presents the human and technical resources needed for collecting, storing, cleaning, transforming, and analyzing data in education. The discussion is framed around a reference architecture for supporting data analytics in education and provides a data governance-driven framework for ensuring data quality. It also addresses how to deal with noisy data (for example, missing data and imputation, duplicate data, and extreme values and outliers) and provides methods for preparing them for meaningful use in education.
