This interdisciplinary course bridges the gap between traditional epidemiology and modern data science, guiding students from basic literacy to advanced AI-augmented workflows without requiring prior coding prerequisites. Adopting a “Zero to AI Co-Pilot” pedagogy, students will utilize cloud-native computing environments to ethically leverage Large Language Models for generating and auditing code in both R and Python, while mastering reproducible research methods using GitHub. The curriculum emphasizes the “last mile” of knowledge translation, equipping learners to transform real-world health data into professional portfolios featuring dynamic scientific writing, interactive web dashboards, and data-driven visual stories. 

In this course on systematic review methodology, students learn key components of a review and acquire skills needed to carry out their own reviews. 

How do we move from identifying associations to answering causal questions?

SPPH 611 offers a rigorous introduction to modern causal inference for graduate students in epidemiology, public health, health services research, and related fields. The course covers key concepts, including directed acyclic graphs, counterfactuals, treatment effect identification, mediation analysis, and probabilities of causation.

Geared toward students working with registries, administrative health data, electronic medical records, cohort studies, and other observational datasets, the course emphasizes practical application. Students will learn to formulate causal research questions, construct causal diagrams, identify adjustment sets, and estimate causal effects using contemporary methods.

The course uses a flipped-classroom format, combining pre-recorded video lectures with interactive discussions, tutorials, and hands-on assignments. By the end of the course, you will be prepared to use causal inference methods in your thesis and research projects.

Recommended background: SPPH 500 and SPPH 503 (or equivalent) and working knowledge of R.

Apply fundamental epidemiological concepts to population and public health datasets. Explore emerging epidemiological methodologies used in population and public health-related research questions.

Either SPPH 604 or SPPH 681C is required to graduate.

Mixing methods within a single study or program involves strategically integrating diverse approaches to leverage complementary strengths and weaknesses. This course is intended for students who wish to go beyond the basics to improve their understanding of mixed methodologies in health research to design and implement mixed method studies. Students will also develop the skills to read, review, and critique mixed methods of proposals and research.

Restricted to PhD students at SPPH. Others by permission of instructor.

Either SPPH 604 or SPPH 681C is required to graduate.

Prerequisites: SPPH 502, 519, 521, 621

This course addresses ethical issues related to health at a population or community level and interventions undertaken by governments or other social organizations to promote it.

This graduate-level course will provide a skills/practice-oriented experience. You will be part of a collaborative team intervening to improve real global (and/or “glocal”) public health outcomes that we co-choose, framed through the problem-solving lens of the United Nations’ Sustainable Development Goals.

The planet is the ultimate health-care system. SPPH 481E introduces students to planetary health, a solutions-oriented, transdisciplinary field that examines how climate change is reshaping the foundations of human health and well-being. This course explores who bears the greatest burden and why, examines the roles of health systems, policy, and advocacy in responding, and connects students to the growing community of practitioners working at the intersection of planetary and human health.

Introduces concepts and methods in the analysis of correlated data, with special emphasis on longitudinal and hierarchical data, including time series data, multilevel data, and spatial and spatiotemporal data.

In this second level epidemiological methods, trainees will apply methods to the development of a fundable research protocol and the analysis and interpretation of real epidemiologic data.

Learn the purposes, context, procedures, and relationships within qualitative health research and methodologies.

This course will review ethical discussions, principles and frameworks in relation to ethical issues that arise in population and public health.

Introductory course on Bayesian statistics and methods, specific focus on Bayesian hierarchical models for correlated health data in general and for small area spatiotemporal disease mapping in particular.  The Bayesian principles, theorem, and key ideas will be introduced with minimum use of mathematics.

This data-driven course focuses on applying supervised and unsupervised machine learning methods and non-standard analytic problems with healthcare data.

Apply advanced statistical methods to analyze sophisticated healthcare-based data problems using large clinical and administrative databases.

Permission of the instructor is required to register for the course.