Analytics and Improvements Library

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