Healthcare Data Analytics Stackable Certificates
The Master of Science in Healthcare Data Analytics program is comprised of three certificates where each certificate can be earned independently or “stacked” together for a personalized degree that best fits your professional and educational goals.
Certificates are offered in Healthcare Data Management, Healthcare Systems and Operations, and Leadership and Strategy in Healthcare Analytics. Both together and separately the certificates in the program teach you to:
- Programming for health analytics
- Quality and operational healthcare management
- Data modeling, visualization and analytics
- Epidemiology and biostatistics
- Machine learning and artificial intelligence
- Leadership and ethics
Certificate in Healthcare Systems and Operations
The Certificate in Healthcare Systems and Operations focuses on operational management, effectively use AI and machine-learning tools, and lead data and systems analysis with a healthcare focus.
This certificate covers a variety of topics including:
- Understanding U.S. healthcare
- Digital health and emerging technologies
- Quality and operational management
Certificate in Healthcare Data Management
The Certificate in Healthcare Data Management focuses at meeting the needs of skilled data management practitioners in the healthcare industry.
The certificate curriculum blends theory and practice across key subjects including:
- Data management
- Governance and analysis
- Programming for healthcare analytics
- Data visualization for stakeholders
- Data entry, retrieval and modeling
Throughout the program, students collaborate with industry experts and use state-of-the-art analytics tools.
Certificate in Leadership and Strategy in Healthcare Analytics
The Certificate in Leadership and Strategy in Healthcare Analytics focuses on leadership and data application for the healthcare industry.
The diverse coursework covers a variety of topics including:
- Leadership and application of ethical models
- Healthcare statistical analysis
- Understanding biases in AI and machine-learning
- Ethical data use and patient privacy
