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Data Modeling

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Learning Objectives

  • Differentiate between data types and how they are used
  • Define primary key, surrogate key, and foreign key, and know what makes a good key
  • Read and interpret an entity-relationship diagram (ERD)
  • Recognize different types of joins and know when to use each
  • Define grain in the context of data modeling and identify in a given scenario or data set
  • Know what indexes are and differentiate between nonclustered and clustered indexes
  • Define table and view and know-how each is used in SAMD
  • Determine when you should use early vs. late binding
  • Define data lake and data warehouse
  • Differentiate between star schemas and third normal form schemas
  • Know why we have SAM framework architecture (Health Catalyst’s data model)
  • Categorize a piece of data in a given scenario as an event, rule, population, pre-metric, metric, or summary metric

Course Description

Effective data-governance solutions begin with a deep understanding of the data. The basic principles of data modeling are essential before diving into the tools Health Catalyst offers that assist in analysis and visualization.

3 modules | 3 CLUs

Course at a Glance

target audience icon Target Learners:

Data Analysts

time to complete icon Average Completion Time:

20–30 minutes for each module

contributor icon Co-development Partner(s):

Health Catalyst

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