HDS 5000 - Foundations in Health Data Science
3 Credits
This dynamic, innovative course immerses first-semester graduate students in the rapidly evolving world of health data science. Focusing on real-world healthcare challenges, students will explore introduction to data, visualization, statistics and analytics, machine learning, and artificial intelligence. Delve into critical topics like personalized medicine, population health trends, and real-time clinical decision-making. Ethical use of artificial intelligence, data privacy, and regulatory frameworks will also be explored, preparing you to be a leader in the future of data-driven healthcare.
HDS 5130 - Healthcare Organization, Management, and Policy
3 Credits
This course offers students a comprehensive exploration of health policy and the U.S. healthcare system, with a focus on recent reforms and emerging trends. It equips students with the knowledge and tools to navigate the evolving healthcare landscape, covering the organization, financing, and regulation of healthcare while critically evaluating key policy initiatives. Special attention is given to the impact of the Affordable Care Act (ACA), ongoing healthcare reforms, and developments such as value-based care, health equity initiatives, and digital health legislation. Students will analyze the challenges of access, cost, and quality in the U.S. healthcare system and explore innovative solutions to improve care delivery and outcomes. Through interactive discussions, case studies, and policy simulations, students will apply evidence-based strategies to real-world scenarios, preparing them to lead and advocate for future healthcare reforms.
HDS 5210 - Programming for Health Data Scientists
3 Credits
Students will be introduced to concepts in computer programming using the Python programming language. Students will learn to conceptualize steps required to perform a task, manipulate files, create loops, and functions. By the end of this course, students will have a basic understanding of computer programming, a working knowledge of the Python programming language, and they will be able to share their scripts to collaborate with other team members.
Attributes: BME Graduate Elective, MPH-Biostatistics, Grad Pol Sci Skills, Social Work PhD Specilization
HDS 5230 - High-Performance Computing and Health Artificial Intelligence
3 Credits
This course explores the introduction of high-performance computing (HPC) and advanced artificial intelligence (AI) in addressing complex health data challenges. Students will gain hands-on experience with scalable computing platforms and AI-driven methodologies to analyze large-scale health data sets. Emphasis will be placed on optimizing computational workflows, deploying AI solutions in real-world health settings, and ensuring ethical and equitable applications in healthcare innovation.
Prerequisite(s): HDS 5310; HDS 5210
Attributes: Social Work PhD Specilization
HDS 5310 - Analytics, Statistics & Visualization Methods in Health Data Science
3 Credits (Repeatable for credit)
This course offers a modern and immersive introduction to analytics, statistics, and visualization methods utilizing Python, SAS, and R programming, tailored for the fast-evolving field of health data science. Through engaging, hands-on projects and real-world applications, students will develop not just a basic understanding of programming but the practical skills to leverage Python, SAS, and R for data manipulation, automation, and collaboration within healthcare settings. This course also emphasizes innovative approaches, including cloud-based coding environments and collaborative tools like GitHub, enabling students to work on team-based projects and share code seamlessly. By the end of the course, students will be able to build functions, automate tasks, and work efficiently in data-driven health research.
Attributes: Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Health Management & Policy, Social Work PhD Specilization
HDS 5320 - Inferential Modeling
3 Credits
Students will learn to conceptualize research questions as statistical models, and parameterize those models from real-world data. The course will start by introducing the linear model, then expand into generalized linear models, nonlinear models, mixed and multilevel models, and Cox survival models. Students will have a working knowledge of how to use statistical models to gain an understanding of the influence of individual predictor variables on health outcomes.
Prerequisite(s): HDS 5310
HDS 5330 - Predictive Modeling and Health Machine Learning
3 Credits
This course will focus on the application of sophisticated machine learning models and statistical techniques to healthcare data. Students will explore algorithms such as regression, decision trees, ensemble methods, neural networks among other deep learning methods. Emphasizing the unique challenges of healthcare data, the course addresses high-dimensionality, missing values, ARIMA, classification, clustering, and visualization equipping students with strategies to optimize model performance and reliability. Through hands-on work with real-world datasets, students will develop the skills to design, implement, and evaluate advanced machine learning models while effectively communicating results to technical and non-technical audiences to support innovation and decision-making in healthcare.
Attributes: Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Biostatistics, Social Work PhD Specilization
HDS 5430 - Health Image Processing and Deep Learning
3 Credits
This level course equips students with advanced skills in visualizing and analyzing complex health data, integrating both traditional data visualization techniques and modern approaches such as image processing and deep learning. Students will learn to transform raw health data, including clinical and imaging data, into meaningful and interactive visual representations that support data-driven decision-making. The course covers image segmentation and edge detection, visual analytics, and dashboard design, alongside innovative applications of deep learning for medical image analysis and diagnostics. Additionally, deep learning predictive modeling techniques will cover CNN, RNN, and LSTM. By the end of the course, students will be proficient in using advanced visualization tools and deep learning techniques to effectively present and interpret health data for diverse healthcare stakeholders.
HDS 5930 - Special Topics
3 Credits (Repeatable for credit)
HDS 5960 - Capstone Experience
3 Credits
This course is designed to offer data science students an opportunity to practice their skills in an industry setting, to learn the roles that various members of a data science team play in an organization, and to begin building a network of professional contacts and references.
Prerequisite(s): ORES 5300; HDS 5210; HDS 5310
Restrictions:
Enrollment limited to students in the MS Health Data Science program.
HDS 5980 - Graduate Independent Study in Health Data Science
1 or 3 Credits (Repeatable for credit)
ORES 2320 - Interprofessional Health Outcomes Research
2 Credits
Offered by the Department of Health and Clinical Outcomes Research (HCOR) within the School of Medicine, this course will provide students the skills vital to developing a measurable research question, investigating the current literature, and incorporating a study design that best answers their research questions. Furthermore, this course will encourage students to look at healthcare research from the scope of social determinants and socio-cultural contexts to better understand health disparities and inequities.
Attributes: IPE - Research, UUC:Dignity, Ethics & Just Soc, UUC:Social & Behavioral Sci
ORES 5010 - Introduction to Biostatistics for Health Outcomes
3 Credits
This course is designed to introduce basic principles of descriptive and inferential statistics. The course will cover fundamental concepts and techniques of descriptive and inferential statistics with application to health outcomes research. This course contributes to the First Dimension by preparing students for advanced study in areas related to Outcomes Research and contributes to the Second Dimension by teaching students tools and methods of research.
Attributes: Health & Rehab Sci Research
ORES 5100 - Research Methods in Health & Medicine
3 Credits
This online course is designed to provide an introduction to the techniques, methods, and tools used for research in the health sciences. Students will obtain an understanding of the research process and scientific method, specific study designs, methods for data collection and analysis. This is a very applied and hands-on course and is focused entirely on the unique aspects of research in the health sciences. This course will utilize Blackboard for all lectures, online discussions, assignment submission, and examinations.
Attributes: Aviation Elective (Graduate), Aviation Research (Graduate), Health & Rehab Sci Research
ORES 5160 - Data Management and Programming in Healthcare
3 Credits
This course provides essential skills for maintaining databases, ensuring data quality, and manipulating data effectively, with a strong focus on practical applications in Python, R, SQL, and cloud computing. Students will engage in hands-on experiences in database design and management, learning to navigate modern data environments relevant to health outcomes research. The course emphasizes the integration of current technologies and best practices in health data management and storage. By fostering proficiency in data tools and methodologies, this course contributes to the development of critical data management skills essential for addressing contemporary challenges in healthcare delivery.
Attributes: MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, Social Work PhD Specilization
ORES 5210 - Foundations of Medical Diagnosis and Treatment
3 Credits
Taught by medical school faculty, this course in an introduction to clinical medicine for graduate students. Basic science concepts include anatomy, physiology, microbiology/hematology, infectious diseases, genetics, immunology, endocrinology and metabolic pathways. Primary organ systems and their associated diseases will also be covered, with special emphasis on their diagnosis and treatment.
ORES 5260 - Pharmacoepidemiology
3 Credits
This course is an introduction to pharmacoepidemiology - the use and effects of drugs in human populations. It provides an overview of the principles of pharmacoepidemiology, sources of pharmacoepidemiology data, and special issues in pharmacoepidemiology methodology. It reviews commonly used study designs, special topics and advanced methodologies for pharmacoepidemiologic studies.
Attributes: MPH-Maternal & Child Health
ORES 5300 - Foundations of Health Outcomes Research
3 Credits
This course introduces students to the methodologies, scientific writing and resources, and data collection processes fundamental to health outcomes research, health measurement, establishing a foundation for evidence-based decision-making in healthcare. Students will explore a range of research designs—learning to select methodologies that best align with specific research objectives and constraints. A major focus will be on ICD codes, clinical terms, data collection techniques, and observational data gathering. Through hands-on projects, students will gain practical experience in designing data collection instruments, evaluating measurement validity and reliability, and addressing challenges like sampling bias and data quality. By the end of the course, students will possess a comprehensive understanding of how to collect, evaluate, and manage data effectively to conduct rigorous outcomes research capable of driving healthcare improvements.
Attributes: Health & Rehab Sci Research, Social Work PhD Specilization
ORES 5320 - Scientific Writing and Communication
3 Credits
The purpose of this course is to take students step-by-step through the process of writing a journal article appropriate for publication in a scientific journal. We will focus on each section of the article for several weeks as students complete assignments related to successfully writing the section and receive feedback on weekly assignments. The last part of the course will focus on taking the research findings presented in the journal article and preparing a poster that could be presented at a research conference. Overall, students will improve their ability to communicate complex research findings in writing to their peers via publication in the peer-reviewed literature and to the broader scientific community through presentation of a poster.
Attributes: MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Biostatistics
ORES 5400 - Pharmacoeconomics
3 Credits
Pharmacoeconomics is one of the cornerstones of Health Outcomes Research. This course is designed to teach clinicians and new researchers how to incorporate pharmacoeconomics into study design and data analysis. Participants will learn how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track and assign costs to different types of health care resources used. This is a required course for the MS in Outcomes Research and Evaluation Sciences but may also be of interest to students in Public Health and Health Administration. This course contributes to the First Dimension by providing students with advanced skills in highly valued research area and contributes to the Second Dimension by developing students’ ability to effectively communication complex information.
ORES 5410 - Evaluation Sciences
3 Credits
This course will examine methods for evaluation of health programs in both organizational and community contexts. Topics include formative research, process evaluation, impact assessment, cost analysis, monitoring outcomes, and evaluation implementation. Strengths and weaknesses of evaluation designs will be discussed. This is a required course for the MS in Outcomes Research and Evaluation Sciences Program but may also be of interest to students in Public Health, Health Administration, and Allied Health. This course contributes to the First Dimension by providing students with advanced skills in the evaluation sciences and contributes to the Second Dimension by developing students’ ability to effectively communicate complex statistical information.
ORES 5430 - Health Outcomes Measurement
3 Credits
This course is designed to introduce students to the principles of health outcomes measurement. Specifically, students will be introduced to the most common measures seen in health outcomes and health services research as well as measure development and assessment of psychometric properties. Topics will include generic vs. disease specific measures, instrument design, scaling, reliability and validity, addressing measurement error, Classical Test Theory, and Item Response Theory. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.
Attributes: Health & Rehab Sci Research, MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Biostatistics, Social Work PhD Specilization
ORES 5440 - Comparative Effectiveness Research
3 Credits
This course is designed to introduce students to the principles of comparative effectiveness research. Specifically, students will be introduced to the concept of comparative effectiveness research, common research methods and statistical analyses, and translation and dissemination. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.
ORES 5550 - SAS Programming I
1 Credit
In the era of big data and outcomes research, skilled scientists can organize, manipulate, and analyze using many different tools. Programming in SAS is an essential skill. This course introduces the SAS environment and programming language. Students will learn data management, descriptive analysis, and statistical inference testing using a hands-on approach. By the end of the course, students will be able to import, organize, and analyze data as well as interpret the results.
Prerequisite(s): (ORES 5010, BST 5000, or BST 5020)
ORES 5970 - Research Topics in Outcomes Research
0-3 Credits (Repeatable for credit)
ORES 5980 - Graduate Independent Study in Outcomes Research
1-3 Credits (Repeatable up to 6 credits)
ORES 6950 - Special Study for Exams
0 Credits (Repeatable for credit)
This Special Study for Exams course indicates that a student will be taking the exams the semester they are registered for.
ORES 6970 - Advanced Research Topics in Outcomes Research
1-3 Credits
ORES 6980 - Graduate Independent Study in Outcomes Research
0-3 Credits
ORES 6990 - Dissertation Research
0-6 Credits (Repeatable for credit)