Data Analytics DIGH 7250

This course revisits health data analytics techniques and reporting requirements to better understand health services and the health of our populations. The course examines concepts associated with health information exchanges, data standards, data aggregation, and population health data. Learners explore strategies for ensuring provenance and integrity of individual and population health informatics sourced across multiple integrated systems. The course discusses techniques to extract, transform, and load (ETL) database functions and their ordering, as well as the integration and harmonization of disparate data sources using ETL principles and software. Learners compare and contrast data management and validation techniques and relational and non-relational database designs (i.e., SQL versus flat file/NoSQL). Learners apply query tools and techniques in collaboration with stakeholders to analyze and interpret data. Learners compare and contrast statistical modelling, machine learning, and deep learning, including when it may be appropriate to choose one approach over another. Learners examine data-mining and analytic techniques (e.g., data visualization, artificial intelligence, natural language processing, machine learning) to optimize health outcomes and decision-making. These techniques will be applied to simulated scenarios. Learners explore, compare, and apply data visualization methods (graphic, geospatial, 3D modelling, dashboards, heat maps) to transform, organize, and present data as meaningful information for diverse audiences. Learners explore how business intelligence and the application of data and technology can be used to optimize decision-making and health outcomes. Learners also examine appropriate implementation of BI tools and strategies.

Domestic fees

$944.28