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BMI 5317 Applied Data Management

3 semester credit hours
Lecture contact hours: 2; Lab contact hours: 3
Web-based instruction
Prerequisites: BMI 5300 or consent of the instructor.
Lab Fee: $30

Proposed course description and learning objectives.

This course provides an introduction and broad orientation to health care data management. Students are introduced to computer programming languages such as R and Structured Query Language (SQL) and have the opportunity to complete module assignments to demonstrate basic competencies. Selected course assignments help students gain skills and experience using Excel. Real world or simulated data sets are used for most module assignments to help students gain an appreciation of the complexity of health data and how data are used in a learning health organization. Students have the experience to complete a data governance project.

Upon successful completion of the course, students will:

  • Demonstrate the use of basic SQL commands when using health data organized in a relational database.
  • Learn the basic syntax of R for data visualization and statistical analysis using health care data.
  • Explore Excel features and functionalities to complete calculations and create data visualizations.
  • Develop a data governance policy

Current Course Description

This course provides a broad foundation for health care data management. Students will develop a data model for a relational database, evaluate the quality of a variety of datasets, utilize common tools to produce actionable information from data, and develop and design processes for effective data and information governance. After the introduction of key theories and concepts across these topics, students will complete hands-on projects.

Upon successful completion of the course, students will:

  • Construct a draft data model and relational database structure to address a health care scenario.
  • Select and utilize SQL and Excel data functionality to produce actionable information for decision-making.
  • Evaluate the quality of data in a dataset.
  • • Develop policies and design processes for effective data and information governance.

Updated: 12/05/2023