Master of Science Program

Learning Health System Oriented MS in Healthcare Data Analytics

Prepares professionals to move from concrete health system questions to defensible analyses and actionable, ethically grounded recommendations.

36 credits, 12 courses Two-course Fundamentals spine 8 "Data for X" courses + Capstone

View curriculum structure Graduate capabilities

Data Analytics Evidence-based decisions

Health Systems Improve patient outcomes

Ethics & Governance Responsible practice

Technical Foundation

Fundamentals I & II Technical Spine

Establish a stable baseline in healthcare analytics so advanced courses assume consistent preparation.

Fundamentals I

Healthcare data structures (encounters, claims, registries, longitudinal records)
Data wrangling, exploratory analysis, basic visualization
Clear interpretation of basic statistics for nontechnical stakeholders

Fundamentals II

Core regression and risk prediction, model assessment
Pragmatic causal reasoning concepts (confounding, adjustment)
Time-oriented analysis and basic forecasting
Reproducible workflows and documentation expectations

Applied Portfolio

The "Data for X" Applied Portfolio

Eight required domains that tie analytics directly to health system decisions.

Patient Experience & Engagement

Analyze patient feedback and engagement data to improve satisfaction and care relationships.

Clinical Care

Support clinical decision-making with evidence-driven analysis of treatment outcomes and care pathways.

Clinical Operations & Efficiency

Optimize resource utilization, workflow efficiency, and operational performance across care settings.

Quality & Safety

Monitor and improve quality metrics, adverse events, and patient safety outcomes.

Clinical Decision Support

Design and evaluate decision support systems that enhance clinical judgment at the point of care.

Population Health

Identify risk patterns and intervention opportunities across defined patient populations.

Health Research & Innovation

Apply analytics methods to support health services research and evidence generation.

Health Systems Strategy

Inform strategic planning and resource allocation with data-driven market and performance analysis.

Courses share the same prerequisite pair and can be taken in flexible order after Fundamentals II.

Core Framework

Learning Health System Orientation

The improvement cycle organizing all applied work throughout the curriculum.

Define the decision and problem

Identify and assess the data

Analyze with appropriate methods

Translate findings into action

Evaluate impact and learn

"The curriculum is structured around how health systems learn and improve, not around disconnected tools."

Integrated Competencies

Four Integrated Competency Strands

Threaded across all applied courses to ensure comprehensive professional development.

Leadership & Organizational Change

Regulation, Governance & Ethics

Visualization & Communication for Clinical and Executive Audiences

Machine Learning & Predictive Modeling Used Safely and Responsibly

Flexible Scheduling

Modular Blocks, Entry Points & Time-to-Completion

Designed for Working Professionals

Cadence: Two short blocks per term (A/B blocks)

Entry Points: Fall, Spring, and Summer

Same curriculum, different pacing—designed for working professionals and predictable scheduling.

Fall Entry ~12 months

Summer Entry ~15 months

Spring Entry ~18 months

Culminating Experience

Capstone I & II

A two-course sequence that integrates all program learning into a health system-relevant project.

Capstone I Planning

Problem definition and stakeholder mapping
Data feasibility assessment
Methods plan development
Governance and ethics constraints

Capstone II Execution

Execution and delivery
Documented assumptions and limitations
Bias and fairness considerations
Implementation pathway

Deliverables

Professional Report Stakeholder-Ready Visualizations Executive Presentation

Graduate Outcomes

Graduate Capabilities

What graduates are prepared to do for health systems upon completion.

Frame real health system questions into answerable analytic workplans

Build end-to-end workflows from messy healthcare data to decisions

Select, justify, and communicate core models with clear limitations

Design analytics aligned with governance, privacy, and fairness constraints

Support operational planning, quality improvement, decision support, and population health strategy

Deliver implementable recommendations, not just analysis