2026 Courses

340.694.89 Power and Sample Size for the Design of Epidemiological Studies I
June 8 - June 17, 2026
Course Format: Online asynchronous
1 credit

140.626.89 Propensity Score and Related Methods for Estimating Causal Effects
June 8 - June 22, 2026
Course Format: Online asynchronous
1 credit

340.701.89 Epidemiologic Applications of GIS
June 8 - June 26, 2026
Course Format: Online asynchronous
2 Credits

340.612.89 Epidemiologic Basis of TB Control
June 8 - June 26, 2026
Course Format: Online asynchronous
2 credits

140.669.89 Introduction to Geospatial Statistics
June 8 - June 26, 2026
Course Format: Online asynchronous
2 Credits

340.765.89 Professional Epidemiologic Methods: Epidemiologic Intelligence and Population Health Assessments
June 8 - June 26, 2026
Course Format: Online asynchronous
2 Credits

340.619 Topics in Pharmacoepidemiology
June 8 - June 26, 2026
Course Format: Online asynchronous
2 credits

340.770.89 Public Health Surveillance
June 8 - June 26, 2026
Course Format: Online asynchronous
3 credits

340.666.89 Foundations of Social Epidemiology
June 8 - June 26, 2026
Course Format: Online asynchronous
3 credits

340.668.89 Topics in Infectious Disease Epidemiology
June 8 - June 26, 2026
Course Format: Online asynchronous
3 credits

340.616.89 Epidemiology of Aging
June 8 - July 2, 2026
Course Format: Online asynchronous
3 credits

340.721.89 Epidemiologic Inference in Public Health I
June 8 - July 10, 2026
Course Format: Online asynchronous
5 credits

340.602 Intermediate Epidemiology 
M T W Th F June 8 - June 12, 8:00 a.m. - 12:20 p.m.
Course Format: Synchronous online taught via Zoom
3 Credits

140.630 Introduction to Data Management 
M T W Th F  June 8 - June 12, 8:00 a.m. - 12:20 p.m.
Course Format: Hybrid course with both a synchronous online section (140.630.49) and an in-person section (140.630.11). You will be able to choose which format when registering.
3 credits

340.626 Intro to Text Analytics Methods for Public Health Research
M T W Th F  June 8 - June 19, 8:30 a.m. - 12:00 p.m.
Course Format: Synchronous online taught via Zoom
3.5 Credits

340.727 Designing and Administering Health Surveys
M T W Th F June 8 - June 12, 8:30 a.m. - 12:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.610.79 Data Visualization
M T W Th F June 8 - June 12, 9:00 a.m. - 12:00 p.m.
Course Format: Synchronous online taught via Zoom
1 credit

140.611 Statistical Reasoning in Public Health I
M T W Th F June 8 - June 17, 1:30 p.m. - 5:00 p.m.
Course Format: Synchronous online taught via Zoom
3 Credits

140.604 Intro to R for Public Health Researchers
M T W Th F June 8 – June 19, 1:30 p.m. - 5:00 p.m. 
Course Format: Synchronous online taught via Zoom
2 Credits

340.632 Excel for Public Health Data: Management, Analysis & Visualization
M T W Th F June 8 - June 12, 1:30 p.m. – 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.613 Data Analysis Workshop I
M T W Th F June 8 - June 12, 1:30 p.m. – 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.606 Survival Analysis
M T W Th F June 8 - June 12, 1:30 p.m. – 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

340.613 Design and Conduct of Clinical Trials
M T W Th F June 8 - June 12, 1:30 p.m. – 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

340.637.11 Discrete Choice Experiments in Public Health Applications
T, June 9, 2026, 8:30am to 12:30pm
Course Format: In-Person
.5 Credits

140.690 Artificial Intelligence for Improved Patient Outcomes
T W Th F June 9 - June 12, 10:00 a.m. - 12:00 p.m.
Course Format: Hybrid course with both a synchronous online section (140.690.49) and an in-person section (140.690.11). You will be able to choose which format when registering.
1 credit

340.634.11 Debunking Misinformation and Combatting Spin in Public Health Research
T, June 9, 2026, 9:00am - 4:50pm
Course Format: In-person
1 credit

340.615 "Git"-ing to Reproducible Code
Th June 11, 1:30 p.m. - 5:00 p.m. 
Course Format: Synchronous online taught via Zoom
0.5 Credits

340.805.79 Application of Epidemiology Study Design & Measurement Principles in Electronic Health Record Data
Th June 11, 8:30 a.m. - 4:50 p.m. 
Course Format: Synchronous online taught via Zoom
2 Credits

340.690 Epidemiologic Approaches to Hearing Loss and Public Health
Saturday, June 13, 2026
8:30 a.m. – 4:30 p.m.
Course Format: Hybrid course with both a synchronous online section (340.690.49) and an in-person section (340.690.11). You will be able to choose which format when registering.
1 Credit

340.725 Methods for Clinical and Translational Research
Saturday, June 13, 2026
8:30 a.m. – 5:00 p.m.
Course Format: Synchronous online taught via Zoom
1 Credit

340.611.89 Using Generative Artificial Intelligence (AI) to Improve Public Health
Monday, June 15 - Friday, June 26, 2026
Course Format: Asynchronous online
Credits: 1

340.604 Introduction to 'Omics
M T W Th  June 15 - June 18, 8:30 a.m. - 12:20 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.635 Practical Genomics: Computational Tools for Cancer Research
M T W Th  June 15 - June 18, 8:30 a.m. - 12:20 p.m.
Course Format: Hybrid course with both a synchronous online section (140.635.49) and an in-person section (140.635.11). You will be able to choose which format when registering.
2 Credits

340.614 Practical Skills for Conducting Epidemiologic Research 
M T W Th   June 15 - June 18, 8:30 a.m. - 12:20 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

340.650 Nutrition Epidemiology
M T W Th   June 15 - June 18, 8:30 a.m. - 12:20 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.697 AI Tools for Data Science and Statistics
M T W  June 15 - June 17, 9:00 a.m. – 10:50 a.m. 
Course Format: Synchronous online taught via Zoom
1 Credit

340.643 Bioinformatic Strategies for Microbiome Data
M T W Th F June 15 - June 26, 10:00 a.m. - 12:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

140.612 Statistical Reasoning in Public Health II
M T W Th F June 17 – June 26, 1:30 p.m. - 5:00 p.m. 
Course Format: Synchronous online taught via Zoom
3 Credits

340.693 Investigation of Outbreaks
M T W Th  June 15 - June 18, 1:30 p.m. – 5:20 p.m.
Course Format: Hybrid course with both a synchronous online section (340.693.49) and an in-person section (340.693.11). You will be able to choose which format when registering.
2 Credits

140.614 Data Analysis Workshop II
M T W Th June 15 - June 18, 1:30 p.m. – 5:20 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

340.716 Implementation Science Concepts, Methods and Study Design
M, Tu, W, Th, June 15 - June 18 1:30 - 5:20pm, 
Course Format: Synchronous online taught via Zoom
2 Credits

340.678 Infectious Disease Transmission Models for Public Health Decision Making
M T W Th F June 22 - June 26, 8:00 a.m. – 12:20 p.m.
Course Format: Synchronous online taught via Zoom
3 Credits

340.622 FDA's Regulation of Prescription Drug Safety and Effectiveness
M T W Th F June 22 - June 26, 8:30 a.m. – noon
Course Format: Synchronous online taught via Zoom
2 Credits

340.676 Bayesian Adaptive Trials 
M T W Th F June 22 - June 26, 8:30 a.m. – noon
Course Format: Synchronous online taught via Zoom
2 Credits

140.605 Introduction to the SAS Statistical Package
M T W Th F June 22 - June 26, 8:30 a.m. – noon
Course Format: Synchronous online taught via Zoom
2 Credits

140.618 AI Programming in Python for Public Health
M T W Th F June 22 - June 26, 8:30 a.m. - 12:00 p.m.
Course Format: This is a hybrid course with both a synchronous online section (140.618.49) and an in-person section (140.618.11). You will be able to choose which format when registering.
2 Credits

340.664.11 Epi Writing Camp I: Findings, Story, Submission 
M T W Th F June 22 - June 26, 9:00am - 12:00pm
Course Format: In-Person
2 credits

340.665.11 Epi Writing Camp II: Finishing Your Manuscript in a Week 
M T W Th F June 22 - June 26, 1:30pm - 5:00pm
Course Format: In-Person
2 credits 

140.669 Leveraging Electronic Health Records (EHR) Data: Opportunities and Challenges for Evidence Generation
M T W June 22 - June 24, 1:30 p.m. - 4:30 p.m.
Course Format: Synchronous online taught via Zoom
1 Credit

140.620 Advanced Data Analysis Workshop
M T W Th F June 22 - June 26, 1:30 p.m. - 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

340.686 Introduction to Systematic Reviews and Meta-Analysis
M T W Th F June 22 - June 26, 1:30 p.m. - 5:00 p.m.
Course Format: Synchronous online taught via Zoom.
2 Credits
140.608 Longitudinal Data Analysis
M T W F June 22 - June 26, 1:30 p.m. – 5:00 p.m.
Course Synchronous online taught via Zoom
2 Credits

140.607 Multilevel Models
M T W Th F June 22 - June 26, 1:30 p.m. - 5:00 p.m.
Course Format: Synchronous online taught via Zoom
2 Credits

Data Analysis Workshop 1 140.613.79
Online Synchronous
Monday, June 8 - Friday, June 12, 1:30pm - 5:00pm
Credits: 2

Intended for students with a broad understanding of biostatistical concepts used in public health sciences who seek to develop additional data analysis skills.

Emphasizes concepts and illustration of concepts applying a variety of analytic techniques to public health datasets in a computer laboratory using Stata statistical software. Learns basic methods of data organization/management and simple methods for data exploration, data editing, and graphical and tabular displays. Includes additional topics: comparison of means and proportions, simple linear regression and correlation.

Data Analysis Workshop II 140.614.79
Online Synchronous
Monday, June 15 - Thursday, June 18, 1:30pm - 5:30pm
Credits: 2

Intended for students with a broad understanding of biostatistical concepts used in public health sciences who seek to develop additional data analysis skills.

Emphasizes concepts and illustration of concepts applying a variety of analytic techniques to public health datasets in a computer laboratory using Stata statistical software. Masters advanced methods of data analysis including analysis of variance, analysis of covariance, nonparametric methods for comparing groups, multiple linear regression, logistic regression, log-linear regression, and survival analysis.

Prerequisite: 140.613 Data Analysis Workshop I

Epidemiologic Inference in Public Health I 340.721.89
Online Asynchronous
Monday, June 8 - Friday, July 10, 2026
Credits: 5

Introduces principles and methods of epidemiologic investigation of disease and other health states. Presents different types of study designs, including randomized trials, cohort and case-control studies; measurement of exposures and outcomes; risk estimation; surveillance; program evaluation; and causal inference. Discusses evaluation measures for screening programs and health interventions. Links epidemiologic inferences with the development of policy. Offers activities that provide experience in applying epidemiologic methods, interpreting findings, and drawing inferences.

340.602.79 Intermediate Epidemiology
Online Synchronous
Monday, June 8 - Friday, June 12, 2026, 8:00am - 12:20pm
Credits: 3

Covers key principles, designs and methods of observational epidemiology studies. Includes a description of general designs of the main observational studies (birth cohort analysis, ecologic studies, cohort, case-based case-control studies, case-control studies within a defined cohort, and case-crossover studies), measures of disease frequency (cumulative incidence, rate and odds) and of association (relative risk, odds ratio), evaluation of confounding and interaction, types of bias, and the most often used methods of adjustment for confounding effects and their assumptions. Employs lectures and group discussions of exercise

Prerequisites: Introductory epidemiology and biostatistics or equivalent work experience

Methods for Clinical and Translational Research 340.725.79
Online synchronous
Saturday, June 13, 2026, 8:30am - 4:50pm
Credits: 1

Reviews essential concepts and methods of translational research. Emphasizes developing skills in the interpretation and application of reports of findings of translational research. Includes topics such as hypotheses and study design, types of data, statistical analyses, and evidence synthesis methods.

340.694.89 Power and Sample Size for the Design of Epidemiologic Studies
Online Asynchronous
Monday, June 8 - Wednesday, June 17, 2026
Credits: 1

Systematically introduces students to sample size and power analysis for the most common epidemiological study designs. Provides participants with the key conceptual elements and practical tools for computing sample sizes to achieve a given level of precision and power in statistical tests.

140.611.79 Statistical Reasoning in Public Health I 
Online Synchronous
Monday, June 8 - Wednesday, June 17, 2026, 1:30 - 5:00pm
Credits: 3

Provides students with a broad overview of biostatistical methods and concepts used in the public health sciences. Emphasizes the interpretation and conceptual foundations of statistical estimation and inference.

Prerequisites: None

140.612.79 Statistical Reasoning in Public Health II
Online Synchronous
Wednesday, June 17 - Friday, June 26, 2026, 1:30 - 5:00pm
Credits: 3

Provides students with a broad overview of biostatistical methods and concepts used in the public health sciences. Emphasizes the application and interpretation of study design and various regression methods.

Prerequisites: 140.611 Stat Reasoning I

Analysis of Longitudinal Methods 
synchronous online 140.608.49: and In person 140.608.11 
Monday, June 22, 2026 - Friday June 26, 2026
2 credits

Covers statistical models for drawing scientific inferences from longitudinal data. Includes topics such as longitudinal study design; exploring longitudinal data; linear and generalized linear regression models for correlated data, including marginal, random effects, and transition models; and handling missing data.

Prerequisites: Intermediate level biostatistics and epidemiology

Application of Epidemiology Study Design & Measurement Principles in Electronic Health Record Data 340.805.79
Online synchronous
Thursday, June 11, 8:30 a.m. - 4:50 p.m.
Credits: 1

Do you know where and how researchers access electronic health record data to answer a research question? Have you wondered what it’s like to work with electronic health record data, but don't know where to start? Electronic health record data are a powerful tool, but using them effectively takes careful consideration of epidemiology principles.

Provides foundational understanding of how electronic health record data can be utilized for epidemiology research. Examines how these data are collected including the data pipelines and data generating mechanisms. Addresses how study design and measurement principles are applied to electronic health record data for accurate research inference. Provides students with a practical set of skills to utilize and design a study using electronic health record data.
 

Bioinformatic Strategies for Microbiome Data 340.643.79
Online synchronous
Monday, June 15 - Friday, June 26, 2026, 10:00am - 12:00pm
Credits: 2

Do you have microbiome sequencing data you need to analyze but don't know how to get started? Are you interested in understanding why microbiome results never seem externally valid?

Introduces key steps for bioinformatic analysis of microbiome data from preparing the data for analysis to visualizing the results. Provides a foundation in ecological concepts including alpha and beta diversity. Explains different methods for finding microbes that differ between environments. Prepares students to plan their own analyses and interpret the results using lectures and hands-on data interpretation exercises.

Discrete Choice Experiments in Public Health Applications 340.637.11
In-Person
Tuesday, June 9, 2026, 8:30am to 12:30pm
Credits: .5 

Discrete choice experiments originated in marketing and economics but have been increasingly used in public health for the last two decades. We will learn about how DCEs are used in public health, how they are designed and some analysis basics.

Acquaints students with the basics of how discrete choice experiments are used in the context of public health research. Provides hands on learning opportunities in formulating experiment design and analysis.

Epidemiologic Applications of GIS 340.701.89
Online asynchronous
Monday, June 8 - Friday June 26, 2026
2 credits

Presents the methods and uses of epidemiology towards the development and application of Geographic Information Systems (GIS) in public health. Emphasizes the potential of GIS as an epidemiological analysis tool for describing the magnitude of priority health problems, identifying health determinants and supporting health decision-making. Specific topics include epidemiological risk assessment and GIS, thematic mapping of unmet health needs, malaria risk assessment and GIS application for identifying public health problems. Includes hands-on experience and laboratory exercises using public domain and ESRI software.

Prerequisites: Basic knowledge of epidemiology and biostatistics and of use of spreadsheets and tabulations; Introduction to Online Learning.  
 

Implementation Science Concepts, Methods & Study Designs 340.716.79
Online synchronous
Monday, June 15 - Thursday, June 18, 2026, 1:30pm - 5:20pm
2 credits

Implementation science is an emerging, multidisciplinary field seeking to translate evidence based interventions and practice into meaningful population impact. Interested in implementation science but not quite sure how exactly it differs from clinical effectiveness research or public health practice? This course will help you to formulate implementation science questions, outcomes and study designs.

Digs into how to conceptualize implementation science questions, define implementation outcomes, and leverage frameworks and designs to achieve public health impact.

Introduction to Geospatial Statistics 140.669.89 
Online asynchronous
June 8 - June 26, 2026
Credits: 2

This course is timely and essential due to the increasing reliance on geospatial data across fields such as climate science, epidemiology, and urban planning. Many students and practitioners who use spatial data would benefit from formal training in its core concepts, data structures, and analytical methods. This course fills that gap by providing a comprehensive foundation in geospatial data handling, visualization, and basic spatial analysis. It equips learners with practical skills and conceptual tools needed to work confidently with contemporary geospatial datasets in research and applied settings.

Provides a practical introduction to geospatial data and methods, with an emphasis on understanding and analyzing real-world spatial datasets. Begins with the basics of geospatial data types and formats, coordinate systems, map projections, and spatial data structures. Covers exploratory spatial data analysis and visualization, including map-making, spatial summaries, and detecting spatial patterns. Introduces core methods for geospatial analysis such as spatial smoothing and interpolation (e.g., variograms, Gaussian process and basic kriging ideas), spatial regression, Bayesian methods, and methods for large spatial data. Includes examples of usage of spatial software such as R, giving students practical skills to import, clean, visualize, and analyze geospatial data for applications in environmental science, public health, and urban studies.

340.604.79 Intro to 'Omics in Public Health
Online synchronous
Monday, June 15 - Thursday, June 18, 2026
Credits: 2

Are you a quantitative scientist who is curious about how –omics can address public health questions? - Do you want to have a better understanding of the underlying biology and measurement tools used in genomics, epigenomics, transcriptomics, and metabolomics? - Do you analyze data and want to understand the process used to generate the data to inform your interpretation of the results or detect potential problems in your dataset?

Introduces quantitative scientists to how “omics” can address public health questions. Reviews basic biology concepts for –omics with a focus on genomics, epigenomics, transcriptomics, and metabolomics. Presents commonly used –omic measurement methods and data preprocessing tools. Discusses challenges that may arise in data analysis due to data measurement issues as well as interpretation of results.

Prerequisites: None

140.607.79 Multilevel Models
Online synchronous
Monday, June 22 - Friday, June 26, 2026
Credits: 2

Gives an overview of "multilevel models" and their application in public health and biomedical research. Multilevel models are statistical regression models for data that are clustered in some way, violating the usual independence assumption. Typically, the predictor and outcome variables occur at multiple levels of aggregation (e.g., at the personal, family, neighborhood, community and/or regional levels). Multilevel models account for the clustering of the outcomes and are used to ask questions about the influence of factors at different levels and about their interactions. Students focus on the main ideas and on examples of multilevel models from public health research. Students learn to formulate their substantive questions in terms of a multilevel model, to fit multilevel models using Stata during laboratory sessions and to interpret the results.

Prerequisites: Previous experience with regression analysis is required

Propensity Score and Related Methods for Estimating Causal Effects 140.626.89
Online asynchronous
Monday, June 8 - Monday, June 22, 2026

Discusses the importance of the careful design of non-experimental studies, and the role of propensity scores and related methods in that design, with the main goal of providing practical guidance on use of sample equating methods. Covers the primary ways of using propensity scores and related methods to adjust for confounders when estimating the effect of a particular “cause” or “intervention,” including weighting, subclassification, and matching. Examines issues such as how to specify and estimate a propensity score model, selecting covariates to include in the model, and diagnostics. Draws examples from across public health. Emphasizes non-experimental studies; however, also discusses applications to randomized trials such as examining levels of adherence and generalizability.

140.606.79 Survival Analysis
Online synchronous
Monday, June 8 - Friday, June 12, 2026
Credits: 2

Introduces fundamental concepts and techniques of survival analysis including censoring, hazard and survival functions, Kaplan-Meier curves and logrank tests. Introduces parametric inferences using the exponential and Weibull distributions. Introduces regression analysis of the Cox proportional hazards model, and its extensions to time-dependent covariates. Discusses special topics, such as non-proportional hazards models and multivariate survival times. Focuses on using data sets from clinical and epidemiological studies to illustrate the introduced statistical methods and to show how to make scientific interpretations from the numerical results. Uses SAS and Stata software for computation. Offers a choice in software based on their own preference when doing exercises. Includes the following topics: censoring, truncation, hazard and survival functions, Kaplan-Meier estimator, log-rank tests, Cox proportional hazards model. 

Prerequisites: None

AI Programming in Python for Public Health 140.618
Hybrid: Online Synchronous 140.618.49 and In-Person 140.618.11
Monday, June 22 - Friday, June 26, 2026, 8:30am - 12:00pm
Credits: 2

This course is essential for anyone interested in leveraging the power of Artificial Intelligence to advance public health. It offers: Cutting-Edge Knowledge: Understand how AI technologies are revolutionizing public health, from epidemic forecasting to personalized healthcare. Practical Skills: Gain hands-on experience with AI tools and methodologies, making you a valuable asset in any health-related field. Broadened Perspectives: Explore the ethical, legal, and social implications of AI in healthcare, preparing you to make informed decisions in your professional practice. Future Preparedness: Equip yourself with the skills and knowledge to stay ahead in a rapidly evolving field, ensuri

Explores the transformative potential of Artificial Intelligence (AI) in public health. Aims at public health professionals, researchers, and policymakers, the course delves into AI’s role in disease surveillance, epidemic prediction, healthcare delivery, and health policy. Gains foundational knowledge in AI concepts, machine learning algorithms, and data analytics. Teaches how AI can address public health challenges, enhance disease prevention strategies, and improve health outcomes through case studies, interactive sessions, and hands-on projects. Emphasizes ethical considerations, data privacy, and the equitable application of AI technologies in diverse health settings.

Prerequisite: Prior programming experience in python

AI Tools for Data Science and Statistics 140.697.79
Online Synchronous 
Monday, June 15 - Wednesday, June 17, 2026, 9:00am - 10:50am
Credits: 1

AI tools can accelerate applied work in data science and statistics, but many analysts use them in ad-hoc ways that are inefficient, unreproducible, or unsafe. This course teaches students how to build structured, repeatable AI-assisted workflows for data pipelines, coding, debugging, documentation, and simulation, with attention to privacy, ethics, context management, and cost.

Introduces practical strategies for effective AI-assisted statistical computing. Emphasizes how large language models (LLMs) and agent-based tools can be integrated into analytic workflows to improve efficiency, reproducibility, and rigor. Teaches design, structured, reliable processes for coding, debugging, documentation, data cleaning, simulation, and pipeline development using AI tools. Includes context management, agent orchestration, LLM selection, and use of Model Context Protocol (MCP) servers. Covers responsible use, including privacy protection, handling of sensitive data, sandboxing, ethics, and cost control. Draw examples from epidemiology, public health, and applied data science. Exercises use R for illustration, but all concepts generalize to any statistical language. Leaves students with a concrete workflow for AI-assisted statistical computing that they can apply immediately.

140.690 Artificial Intelligence for Improved Patient Outcomes 
Hybrid: Online Synchronous 140.690.49 and In-Person 140.690.11
Tuesday, June 9 - Friday, June 12, 2026, 10:00am - 12:00pm
Credits: 1

Artificial Intelligence (AI) will be used to improve health outcomes, but only if we use rigorous science to evaluate its impact. Develop the skills to distinguish between AI hype and AI progress. Advance your career by learning valuable AI skills. Learn how to publish your AI success stories in top medical journals.

Equips students with the essential skills to build and evaluate AI and predictive modeling tools in medicine. Emphasizes practical implementation and rigorous evaluation to address unique challenges in healthcare. Addresses the limits of AI’s potential to benefit patients and presents actionable insights to overcome these challenges.

Prerequisite: None

Introduction to Text Analytics Methods for Public Health Research 340.626.79
Online synchronous
Monday, June 8 - Thursday, June 18, 2026, 8:30am - 11:50am
Credits: 3.5

Provides practical skills for analyzing unstructured text data in public health. Teaches how to clean, structure, and extract meaningful information from text using text-analytics methods, along with an introduction to Natural Language Processing and AI tools. Emphasizes hands-on coding and applies text-derived insights to epidemiologic research.

Using Generative Artificial Intelligence (AI) to Improve Public Health 340.611.89
Online Asynchronous
Monday, June 15 - Friday, June 26, 2026
Credits: 1

The emergence of practical, everyday uses for artificial intelligence tools has captured the imagination of the public, for both good and bad. How, though, can artificial intelligence and generative AI tools like ChatGPT, Midjourney, or even Microsoft Copilot be used to improve the practice of public health? Can they be used safely and ethically? In this introductory, hands-on course, we'll look at these questions through a variety of lenses, offering opportunities to build skills in using generative AI tools in your own public health work.

Introduces students to core concepts in the utilization of generative artificial intelligence (AI) tools. Explores ethical, financial, and policy-based issues in the application of generative AI to public health. Contrasts the accuracy and reach of generative AI with the potential for both misinformation and problem-solving. Enables students to develop skills in utilizing generative AI tools for public health research and practice.

Prerequisite: Introduction to Online Learning 

Professional Epidemiologic Methods: Epidemiologic intelligence and Population Health Assessments 340.765
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 2

Epidemiologic intelligence, based on valid and reliable data, is essential to assess, measure, and describe the health and wellbeing of different populations, as well as their health risks, health needs, and health outcomes. Critical analysis of health information for the purpose of describing population health, depends on thorough understanding of various routine and non-routine data sources, information systems, and tools.

Focuses on practical skills for epidemiological assessments of population health, which includes methods for monitoring epidemiological profiles and health trends, using public health information systems for measuring health burden, developing epidemiological profiles, and conducting health situation analyses.

Public Health Surveillance 340.770
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 3

Acquaints students with Public Health Surveillance, which is a core public health function essential for understanding and monitoring population health. Covers the theory, data collection methods, data analysis techniques, and presentation strategies of the systematic, continuous, analysis and interpretation of population health data to inform planning, implementation, and evaluation of public health practice. Students identify the different types of surveillance, and how each is applied in varied settings. Practical experiences/labs involve creating data collection tools, and reviewing how they can be applied in practice. Real-world surveillance data is used to illustrate methods for analysis, and how surveillance data should be presented to different audiences. Guests who are coordinating and conducting surveillance in different community settings lead interactive discussion sessions.

Bayesian Adaptive Trials 340.676.79
Online Synchronous
Monday, June 22 - Friday, June 26, 2026

Presents Bayesian adaptive designs and teaches students the skills and considerations necessary to construct such designs. Examines the operating characteristics of Bayesian adaptive designs and the benefits and costs of interim analyses, in particular within the regulatory framework.

Design and Conduct of Clinical Trials 340.613.79
Online Synchronous
Monday, June 8 - Friday, June 12, 2026

Introduces clinical trial design in the context of epidemiological concepts, covers various topics in the design and conduct of clinical trials, and profiles clinical trials that illustrate these issues. Includes topics such as the definition and history of clinical trials; trial designs, including phase III-IV, cross-over, factorial, and large, simple designs; internal and external validity; controls, randomization, and masking; ethical issues; introductions to data collection and management and analysis principles; monitoring of trials for safety and efficacy; and use of clinical trial data in healthcare decision-making.

Advanced Data Analysis Workshop 140.620.79
Online Synchronous
Monday, June 22 - Friday, June 26, 2026
Credits: 2

Covers methods for the organization, management, exploration, and statistical inference from data derived from multivariable regression models, including linear, logistic, Poisson and Cox regression models. Applies these concepts to two or three public health data sets in a computer laboratory setting using STATA statistical software. Includes topics: generalized linear models, product-limit (Kaplan-Meier) estimation, Cox proportional hazards model.

Data Visualization 140.610.79
Online Synchronous
Monday, June 8 - Friday, June 12, 2026 9:00am -noon
Credits: 2

Students will learn the foundational principles and practical skills to effectively visualize and interpret data, enhancing their ability to communicate complex statistical findings clearly and persuasively. In fields like epidemiology, biostatistics, and data science, where decisions are often informed by large and complex datasets, visualization is essential.

Explores the principles and practices of effective data visualization. Emphasizes the role of visualization in statistical reasoning, modeling, and communication. Prepares students to create clear, accurate, and impactful visualizations for audiences in both academia and industry. Discusses strategies for conveying complex ideas with data, addressing common pitfalls and challenges. Focuses on examples from epidemiology, public health, and applied data science to demonstrate real-world applications. Practical applications will be taught primarily using R, ggplot2, and supporting libraries. Techniques using Python, Stata, and time series dashboard software will also be addressed.

Prerequisite: Experience with elementary statistics and linear modeling and basic familiarity with R and RStudio.

Excel for Public Health Data: Management, Analysis & Visualization 340.632.79
Online Synchronous
Monday, June 8 - Friday, June 12, 2026
Credits: 2

Excel isn't just for finance. In public health, it is the "first responder" for tracking outbreaks, managing surveillance data, and monitoring community health. Learn to turn raw numbers into clean datasets, insightful analyses, and clear charts that support evidence-based decisions at the local health department level. Build the data-management and visualization skills that agencies expect, but rarely teach… no coding needed. Want to hit the ground running on day one of your public health job? This course gives you practical tools to work with real-world data from day one.

This course provides hands-on Microsoft Excel training tailored for local public health work. Students will learn practical skills to import, clean, manage, and document datasets (whether from surveillance reports, community health assessments, immunization records, or program evaluation data). Through guided exercises, the student will learn foundational tools (e.g., data validation, filters, pivot tables, basic formulas) and learn how to structure data for analysis while maintaining clarity and quality (e.g., creating data dictionaries, consistent coding, and clean formatting).

"Git"-ing to Reproducible Code 340.615.79
Online Synchronous
Thursday, June 11, 2026,  1:30pm - 5:00pm
Credits: .5

Do you need to share code for your projects but aren't sure how? Have you heard of GitHub, but don't know how you can use it in your research? Code sharing is critical to modern science and sometimes successful publication. There are lots of free tools for code sharing, but they require a time investment to use.

Reproducible research relies on open data and available code. However, there is little training on the very specific hands-on nature use of tools to share reproducible analytical code. This course focuses on the use of GitHub to version, share, critique, and publish analysis code based in a practical application. At the end of the course, students should be able to create and share their own reproducible repositories

Introduction to Data Management 140.630
Hybrid: Synchronous Online 140.630.49 & In-person 140.630.11
Monday, June 8 - Friday, June 12, 2026, 8:00am - 12:20pm
Credits: 3

Introduces students to the principles and skills required to collect and manage research data in a public health setting. Focuses on tools for collecting data that range from spreadsheets to web-based systems, database fundamentals, data collection form design, data entry screen design, proper coding of data, strategies for quality control and data cleaning, protection and sharing of data, and integrating data from external sources. Includes practical and hands-on exercises that require some entry-level computer programming.

Introduction to R for Public Health Researchers 140.604.79
Online Synchronous
Monday, June 8 - Friday, June 19, 2026 1:30pm - 5:00pm
Credits: 2

For those who have little to no familiarity with the R programming language and want to learn more about how to use R to import, wrangle, analyze, and visualize data. Provides “hands-on” training for analyzing data in the R statistical software package, a popular open-source solution for data analysis and visualization. Covers data input/output, data management and manipulation, and constructing useful and informative graphics. Geared towards individuals who have never used R or have a little familiarity. 

Prerequisites:  Previous exposure to hypothesis testing and statistical modeling

Introduction to the SAS Statistical Package
Hybrid: Synchronous online (140.605.49) and (140.605.11)
Monday, June 22 - Friday, June 26, 2026, 8:30am - 12:00pm

Through this course, students become adept users of the SAS statistical package, mastering the skills needed for effective data management, data manipulation, and data analysis. Students learn how to document work, and make the work replicable. Discusses graphical techniques for displaying data. While this course uses the SAS statistical package exclusively, much of the technical knowledge and some of the computing techniques are applicable to any statistical package. No prerequisites.

Practical Genomics: Computational Tools for Cancer Research 140.635
Hybrid: Synchronous Online 140.635.49 and In-person 140.635.11
Monday, June 15 - Thursday, June 18, 2026, 8:30am - 12:20pm
Credits: 2

Designed for researchers and clinicians, this workshop introduces essential techniques for analyzing complex genomics datasets using R and Bioconductor. Learn to manipulate, visualize, and interpret data with real-world cancer biology examples, empowering you to integrate computational tools into your research workflows.

Provides an introduction to computational analysis of genomics datasets with applications in cancer research. Includes hands-on training in organizing, analyzing, and visualizing data using R, RStudio, and Bioconductor. Covers data manipulation, single-cell genomics, and differential gene expression analysis. Features live coding demonstrations, guided exercises, and capstone projects. Emphasizes real-world examples relevant to cancer biology and practical skills for integrating computational genomics into research workflows.

Prerequisite: Basic knowledge of molecular biology, genomics, and cancer biology Completion of preparatory tasks to set up cloud computing accounts Some familiarity with R and RStudio

Debunking Misinformation and Combatting Spin in Public Health Research 340.634.11
In-Person
Tuesday, June 9, 2026, 9:00am - 4:50pm
Credits: 1

In recent years, we have seen an uptick in misinformation and the subsequent rising distrust of public health. Do you find yourself at a loss in how to address this as a public health practitioner? This course will describe the historical and current context of misinformation and prepare you to recognize common tactics for more effective communication around public health. Drawing from epidemiology, biostatistics, misinformation studies, and communication, we will introduce resources and work through exemplar topics to set the foundation for this important work in public health.

Provides a broad understanding of the historical and current context of misinformation in public health. Reviews current frameworks for identifying and understanding misinformation, as well as preventing its propagation in communication of public health findings. Describes common statistical and debate techniques utilized in misinformation to enable students to identify misinformation. Employs frameworks and techniques to exemplars of public health misinformation relating to current topics in epidemiology.

Epi Writing Camp I: Findings, Story, Submission 340.664.11
In-Person
Monday, June 22 - Friday, June 26, 2026, 9:00am - 12:00pm
2 credits

Whether your results are ready or in progress, this course helps turn research into clear manuscripts. Faculty with extensive experience teach through short, dynamic lectures using examples, structured writing time, and peer/instructor feedback. We cover writing manuscript sections, formatting data for clarity, and revision workflows. We'll discuss using AI in manuscript writing (outlining, clarity edits, consistency checks, etc.). The goal: leave with a coherent draft or scaffold to finalize when results are ready. Enrollees may register for our afternoon course for those with findings and who want to focus on finishing manuscripts.

Offers short examples, guided writing, and structured peer/instructor feedback where students will work on their own project or a provided dataset. Focuses on the essentials of clear, efficient scientific writing: journal fit, aligning the story with your analyses, numerically consistent text/tables/figures, concise Methods/Results, effective abstracts, and revision/response-to-review. Covers the responsible, effective use of AI for outlining, clarity edits, and consistency checks. Emphasizes this format output: brief mini-lectures paired with writing blocks and an optional afternoon bootcamp for those aiming to finish a manuscript (students should enter with a research question and either preliminary outputs or a plan; those without results will build a complete scaffold.)

Epi Writing Camp II: Finishing Your Manuscript in a Week 340.665.11
In-Person
Monday, June 22 - Friday, June 26, 2026, 1:30 - 5:00pm
Credits: 2

You have results—now finish the paper. This intensive, faculty-accompanied course turns completed findings into a submission-ready manuscript with structure, accountability, and frequent feedback. Short, targeted lessons feed directly into writing blocks with daily deliverables, peer review, and 1:1 guidance. We’ll craft a tight narrative, pick a journal, meet reporting standards, polish figures/tables, and manage coauthor input. Leave with a complete draft and core submission materials—plus a concrete timeline to submit.

Offers a five-day, hands-on sprint to turn completed analyses into a submission-ready first-author paper. Adds structure, accountability, and continuous feedback to students in the companion writing course. Pairs a short goal-setting session with focused writing blocks, concrete milestones, and peer/faculty review. Selects a target journal, aligns story and results, polishes figures/tables, meets reporting standards, and sharpens Methods, Results, Introduction, Discussion, title, abstract, and references. Leaves with a polished draft tailored to your journal, core submission materials (cover letter, checklists, data/code statements), and a concrete plan to submit.

Prerequisite/Co-requisite: Epi Writing Camp I: Findings, Story, Submission

Introduction to Systematic Reviews and Meta-Analysis 340.686.79
Online synchronous
Monday, June 22 - Friday, June 26, 2026, 1:30pm - 5:00pm 
Credits: 2

Covers methods for performing systematic reviews and meta-analyses, including building a team, formulating a research question and hypothesis, developing a search strategy, abstracting and collecting data, assessing the risk of bias of randomized controlled trials (using the latest Risk of Bias tool 2.0), and synthesizing the evidence both qualitatively and quantitatively. Acquaints students with a few practicalities of conducting a systematic review through interactive hands-on exercises, including using Stata software to perform meta-analyses. 

Epidemiologic Approaches to Hearing Loss and Public Health
Hybrid: Synchronous Online 340.690.49 and In-person 340.690.11
Saturday June 13, 2026, 8:30am - 4:30pm
Credits: 1

Hearing loss impacts two-thirds of adults over the age of 70 years old and is associated with important gerontological outcomes including dementia and falls. Hearing aids represent the most common approach to addressing hearing loss. However, less than twenty percent of persons with hearing loss own and use hearing aids. This course will provide the foundational knowledge to investigate and address hearing loss as a public health concern.

Introduces biologic, epidemiologic and clinical aspects of aging-related declines in the auditory system. Demonstrates methods of assessment of auditory function for epidemiologic studies. Reviews current epidemiologic knowledge of sensory function and aging-related outcomes in older adults, including the epidemiology and consequences of dual sensory loss. Presents areas for future research and opportunities for intervention and prevention.

Prerequisite: None

Epidemiology of Aging 340.616.89
Online Asynchronous
Monday, June 8 - Thursday, July 2, 2026
Credits: 3

Addresses the rapidly increasing need for specialized knowledge among epidemiologists in order to effectively promote the health of the aging society in the US (in 2020, 20% of the US population will be 65 or older). Introduces the epidemiology of aging and age-related disorders, including overviews of the public health impact of an aging society and the demographics and biology of aging. Covers the descriptive and analytic epidemiology of prevalent chronic conditions in the aged, methodologic challenges essential to consider in research on older adults, and strategies for prevention of age-related disorders.

Prerequisite: 1 graduate course each in Epidemiology and Biostatistics (340.601 & 140.621 recommended)

Foundations of Social Epidemiology 340.666.89
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 3 

Presents applications of social epidemiologic concepts, introduced through weekly online lectures and readings, and the use of discussions and case studies. Prepares students to understand and appreciate the contribution of social factors to disease etiology, course, and the distribution of states of health in populations. Reviews the conceptual and theoretical underpinnings of social epidemiology from an historical perspective. Focuses on the scientific findings in the field from the 1970's until today; the influence of social context on behavior is well known and forms the backbone for most health promotion interventions. Delineates how the social environment influences behavior by shaping norms, reinforcing social control, providing environmental opportunity, and coping strategies.

Prerequisite: 340.601, 340.721, or 340.751 or equivalent. Students must complete Introduction to Online Learning prior to enrolling in this course. Students must have some background in social science theory and methods. Students who have not had college level social science (sociology, psychology, anthropology) should consult with the course director before signing up for this course.

Nutrition Epidemiology 340.650.79
Online Synchronous
Monday, June 15 - Thursday, June 18, 2026 8:00am - 12:00pm
Credits: 2

Provides an introduction to the methodological issues involved in the design, conduct, analysis, and interpretation of studies investigating the relationship between nutritional status, diet, and disease. Emphasizes issues such as intraindividual variation, measurement of error, misclassification, correlated variables, population homogeneity, and the use of group versus individual data. Covers the selection and use of dietary and nutritional status assessment methods appropriate for different study designs, and some experience in their use and interpretation will be provided. Emphasizes the impact of methodological issues, and the type of study design, on interpretation and conclusions from research in nutrition epidemiology.

Topics in Pharmacoepidemiology 340.619.89
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 2

Most adults in the United States and world-wide use daily medications. Learn the best methods to study the utilization, safety, and effectiveness of medicines.

Introduces the key elements of pharmacoepidemiology. Explores the utilization and effects of drugs in large numbers of people and the application of epidemiological methods to pharmacological issues. Focuses on questions of drug safety and effectiveness, concentrating on clinical patient outcomes and on evaluating the use of therapies. Applies the research methods of clinical epidemiology (e.g., randomized trials, cohort studies, case-control studies, use of secondary data, attention to biases and confounding, effects of non-adherence, active and passive surveillance for adverse events) to study medication exposures and outcomes.

FDA's Regulation of Prescription Drug Safety and Effectiveness 340.622.79
Online Synchronous
Monday, June 22 - Friday, June 26, 2026, 8:30am - 12:00pm
Credits: 2

The safety and efficacy of prescription drugs is critical for the prevention and treatment of diseases and health conditions. The U.S. Food and Drug Administration (FDA) has substantial regulatory oversight over prescription drugs and makes decisions about whether to approve a drug based on the epidemiologic evidence and relevant legal standards. These decisions have significant implications for patients, healthcare professionals, and public health. This course is intended to help public health professionals understand how FDA determines whether to approve a prescription drug as safe and effective and the epidemiologic evidence underlying these FDA determinations.

Reviews regulatory standards and how FDA evaluates epidemiologic evidence to assess a drug’s efficacy and safety. Examines the quality and quantity of epidemiologic evidence needed to demonstrate substantial evidence of effectiveness for a drug and how FDA assesses and balances the drug’s harms and risks based on the relevant evidence. Reviews the accelerated approval pathway as a regulatory option for a drug’s approval, and how FDA applies this pathway based on the epidemiologic evidence. Assesses case studies of FDA actions applying these methods of assessment and standards.

Epidemiologic Basis for Tuberculosis Control 340.612.89
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 2

Considers epidemiologic principles relevant to addressing the global burden of tuberculosis (TB). Examines diagnosis of TB infection and disease; risk factors; prevention by case-finding, treatment, vaccination, and preventive therapy; TB in children and during pregnancy, drug-resistant TB, cost-effectiveness and modeling; policy and advocacy; and post-TB sequelae. Offers lectures, group discussions, and review of the TB literature as the primary components.

Infectious Disease Transmission Models for Public Health Decision Making 340.678.79
Online Synchronous
Monday, June 22 - Friday, June 26, 2026, 8:00am - 12:20pm
Credits: 3

As the COVID-19 pandemic has demonstrated, increasingly infectious disease transmission models are being used to inform public health decision making. Do you want to learn how to make and use transmission models? Do you want to learn how to interpret and communicate models?

Develops, implements, and interprets mathematical and statistical transmission models through lectures and hands on practice in order to answer public health questions. Encourages the critical evaluation of models and assumptions as well as how to communicate model results and uncertainty.

Prerequisite: None

Investigation of Outbreaks 
Hybrid: synchronous online 340.693.49 and In person 340.693.11 
Monday, June 15 - Thursday, June 18, 2026, 1:30pm - 5:20pm 
Credits: 2

Teaches how to detect, investigate, and interpret disease outbreaks. Focuses on application of epidemiological skills to develop hypotheses relevant to understanding source or reservoirs of infection, modes of spread and possible control measures. Includes simple epidemiological approaches for examining field data on outbreaks and deriving inferences. Reviews the main factors involved in the occurrence of an outbreak and steps in investigating an epidemic. Uses data from large and small epidemics to illustrate the main concepts and terminology.

Prerequisite: Students must have basic knowledge of infectious diseases. Introductory epidemiology and biostatistics required or equivalent work experience.

Topics in Infectious Disease Epidemiology 340.668.89
Online Asynchronous
Monday, June 8 - Friday, June 26, 2026
Credits: 3 

Introduces the basic methods for infectious disease epidemiology and case studies of important disease syndromes and entities. Methods include definitions and nomenclature, outbreak investigations, disease surveillance, case-control studies, laboratory diagnosis, molecular epidemiology, and dynamics of transmission. Case-studies focus on acute respiratory infections, diarrheal diseases, hepatitis, tuberculosis, sexually transmitted diseases, malaria, and other emerging infections.

Prerequisite: Introduction to Online Learning

Designing and Administering Health Surveys: A Practical, Skills-Based Approach 340.727.79
Online synchronous
Monday, June 8 - Friday, June 12, 2026, 8:30am - 12:00pm
Credits: 2
 

Leveraging Electronic Health Records (EHR) Data: Opportunities and Challenges for Evidence Generation 140.669.79
Online Synchronous
Monday, June 22 - Wednesday, June 24, 2026, 1:30pm - 4:30pm
Credits: 1

Over recent decades, observational data have become vital for advancing biomedical research and clinical decision-making. Among these, electronic health record (EHR) data stand out for their richness and complexity, capturing diverse longitudinal clinical information such as demographics, diagnoses, treatments, and test results. EHR data offer immense opportunities for generating actionable evidence and driving healthcare innovation but also present challenges like missing data, measurement errors, and biases.

Explores the practical use of observational data, with a focus on EHRs, in biomedical studies. Teaches the challenges of working with these data and the latest methodologies to address them, gaining insights into their potential and limitations for evidence generation.

Prerequisite: None

Practical Skills for Conducting Epidemiologic Research 340.614.79
Online Synchronous 
Monday, June 15, 2026 - Thursday June 18, 8:30am - 12:20pm
Credits: 2

Covers applications of epidemiologic principles in the conduct of observational studies as taught in advanced epidemiologic methods. Focuses on developing skills to conduct and manage a research protocol, monitor data collection, manage data and disseminate results. Covers components of a clinical research team, responsibilities, expertise and tasks study members perform, and organizational, logistical and attitudinal issues that need to be addressed in producing an effective research group. 

Topics include infrastructure needed for single-site and multi-site studies, selection bias and analytical intent in the determination of populations and methods for recruitment, development of a manual of operations and forms for data collection and administration, data management tools, implementation and review of quality assurance, specimen repository tracking, and useful statistics for evaluating the progress of the study.

Prerequisite: None