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Marquette online health care data analytics curriculum
Our interdisciplinary online M.S. in Health Care Data Analytics courses will help you gain the insight and experience you need to make a difference in the world of health care. Marquette’s online program combines equal parts computer science with health care context to prepare you for an in-demand role as a data analytics professional in the booming health care field.
Pursuing your education through a Jesuit institution also means your courses will teach you to reflect on the ethical and social issues inextricable from the modern health care industry. Marquette’s health care data analytics curriculum will help you to become a meaningful bridge between individuals, big data and the health care industry.
The program allows me to take my analytics abilities to a higher level. I especially love that I had the opportunity to learn R, Tableau, Power BI and STATA.
Jose Quiroz, M.S. in Health Care Data Analytics '22 candidate
Data analytics core
Build your foundation with computer science courses focused on computer science and how to use data to tell a story.
COSC 5500: Visual Analytics (3 credit hours)
Focuses on developing data products using the Javascript/D3 framework by combining concepts from human-computer interaction, visualization and design. Also focuses on model visualization, interpretation, A/B testing and design thinking.
COSC 5820: Ethical and Social Implications of Data (3 credit hours)
An introduction to the ethical and social consequences of collecting, curating and analyzing data in academia, public and private contexts. A socio-technical stance is taken in unpacking issues of algorithmic biases, fairness, transparency and accountability.
COSC 6510: Business Intelligence (3 credit hours)
Foundational topics in business intelligence. Includes properties and benefits for business intelligence and methodology for the development of business intelligence solutions. Examines technology employed for managing data and creating visualizations and dashboards. Topics include developing a business case, evaluating performance and managing data. Presents overview of data architectures commonly used in business intelligence solutions and includes exercises using common techniques for prediction and time series analysis.
COSC 6520: Business Analytics (3 credit hours)
Foundational topics in the analysis of data from a business perspective. Includes methodology for the development of business analytics systems. Examines technology employed for business analytics in a variety of industry segments and the benefits derived from business analytics.
Foundations of text and data mining techniques commonly used for classification, clustering and prediction. Students are presented techniques for developing a business case, evaluating predictive performance and managing data. Includes exercises using analytic technology and a project to apply analytics to a customer application. Students are advised to complete COSC 6510 Business Intelligence before attempting COSC 6520.
Foundations of text and data mining techniques commonly used for classification, clustering and prediction. Students are presented techniques for developing a business case, evaluating predictive performance and managing data. Includes exercises using analytic technology and a project to apply analytics to a customer application. Students are advised to complete COSC 6510 Business Intelligence before attempting COSC 6520.
COSC 6570: Data at Scale (3 credit hours)
Combines ideas from parallel databases, distributed systems and programming languages to analyze data at scale. Relevant technologies are introduced and taught in an accessible and inclusive way. Some examples include cloud computing, SQL and NoSQL databases, MapReduce ecosystem, Spark and its contemporaries and graph databases.
Health care context
Health care courses help you learn how to apply your data methodology knowledge to the health care setting.
HEAL 6830: Quality Improvement Science in Health Care (3 credit hours)
Explores improvement science including quality and patient safety theories, models, methods and tools. Application of measurement, data management and analysis to quality improvement and patient safety challenges.
HEAL 6835: Health Care Informatics, Technology and Professional Issues (3 credit hours)
Examines current health care realities, with an emphasis on the use of technology for policy, regulation, collaboration and interdisciplinary practice issues. Includes information technology applications in healthcare administration, clinical practice and education.
HEAL 6840: The Environment of Heath Care Delivery (3 credit hours)
Overview of the U.S. health care system, environmental influences, and current models for health care delivery (e.g., fee for service, modified fee for service, managed care, capitated care, IPOs, HMOs), and the ascendancy/descendancy of various models in different geographic regions and in response to economic incentives.
HEAL 8015: Applied Statistics for Health Sciences (3 credit hours)
Overview of applied statistics, including descriptive statistics, probability, sampling, power calculations, bivariate parametric and non-parametric analysis, and introduction to multivariate analysis. Emphasis on appropriate statistics for study design, level of measurement, and interpretation of results.
Practicum
HEAL 6965: Applied Health Data Analytics Practicum (3 credit hours)
Application of advanced health care data analytics knowledge in a mentor guided experience in a practical setting. Prerequisite: HEAL 8015, or a statistics course approved by the program.
ADMISSIONS DEADLINES
Nov
18
Priority application deadline
November 18
Spring 2023 term
Jan
6
Application deadline
January 6
Spring 2023 term
Jan
17
Next start
January 17
Spring 2023 term
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