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學生對 科罗拉多大学系统 提供的 Clinical Data Models and Data Quality Assessments 的評價和反饋

28 個評分
8 個審閱


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....



1 - Clinical Data Models and Data Quality Assessments 的 8 個評論(共 8 個)

創建者 Mor K

Jul 22, 2019

Gives a great understanding of ETL and the surrounding concepts from Zero. I personally found that a little boring, but for a total newbie to data and computers this is a perfect course.

創建者 Angela B

May 14, 2019

Great course.

創建者 Vu T T T

Sep 14, 2019

Good instructor who took time to explain and walked through each steps of the ETL process. Highly recommended.

創建者 qianmengxiao

Jun 24, 2019

Good course, but the video is a bit too long, split into shorter video course

創建者 Allison B

Jul 25, 2019

Teaching was excellent, but I feel that the peer reviewed feedback model for the final project may not be the most helpful since they're the only ones looking at your work (as opposed to an instructor). Additionally, there were quite a few typos in the quizzes

創建者 William H

Mar 16, 2019

Material was presented fairly well for the most part. The lecture videos had some small editing errors which looked a bit unprofessional. The workload was also a bit unbalanced - there was very little structured hands-on training prior to the capstone project which can appear daunting at first.


Apr 08, 2019

Found very difficult to finish.

創建者 M. B T

Aug 26, 2019

Contenu très intéressant dans l'ensemble, découverte des bases publiques et des concepts construits autour de ces éléments de la connaissance. Cette formation serait parfaite si la présentation était plus claire, en tout cas pour un français, moins répétitive et plus approfondie sur certains points. Les perspectives en connaissance partagée et connaissance induite (ML) seraient à explorer.