Course Detail
Course Components:
Course description and teaching/learning methods: This half semester course (7 modules/sessions) will provide an introduction to understanding general concepts of data quality that can be applied to data sources from multiple domains and practical application of these concepts with a variety of biomedical data sources. In the first two module students will learn the universal principles of data quality, common measures of data quality and the role data quality has in understanding biomedical data. Specifically, students will learn about intrinsic data quality, contextual data quality, representational data quality and accessibility data quality when using and/or evaluating data source(s)1. Building on this foundation, in the remaining modules of the course, students will be exposed to a variety of biomedical data types in the context of research activities: predictive analytics, Natural Language Processing (NLP), genomic analysis, geospatial/environmental and clinical/translational research. Each research activity will be presented as individual course modules by domain experts and will include: How to access the data, What metadata is available or missing for assessing data quality, In what format(s) the data exists, What data cleaning can be done, Data transformations that may be required, How to analyze the data to obtain results, and Students will be able to work with, and evaluate, data from the data source selected for the module.