This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
The ETL Task
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Skills You'll Learn
Database Design, Data Quality, Data Validation, Data Modeling, Clinical Data Management, Health Informatics, Data Transformation, Relational Databases, Data Integration, Extract, Transform, Load, SQL
Reviews
4.2 (64 ratings)
- 5 stars53.12%
- 4 stars26.56%
- 3 stars10.93%
- 2 stars4.68%
- 1 star4.68%
MK
Nov 5, 2019
What a great course!! Kudos to the professor for being so detail oriented!! I learned a great deal about the clinical data models from this course!!
JJ
Sep 14, 2019
Good instructor who took time to explain and walked through each steps of the ETL process. Highly recommended.
From the lesson
Techniques: Extract-Transform-Load and Terminology Mapping
This module teaches learners about the processes and challenges with extracting, transforming and loading (ETL) data with real-world examples in data and terminology mapping.
Taught By
Laura K. Wiley, PhD
Associate Professor
Michael G. Kahn, MD, PhD
Professor of Clinical Informatics