Academics & Programs

BSTA 101 | Population Health Data Science II | 3 cr.
Section 010 | TR 07:55 - 09:10 | CRN: 45350
In this course students expand their statistics and machine learning toolkit by learning how to compare univariate distributions, build traditional regression models for continuous and binary data, explore supervised learning methods such as: Tree-based learning, KNN/Collaborative filtering, and Feed forward Neural networks, and understand how to manipulate, ask, and answer questions from big datasets. Students will be expected to propose a population health project mid-semester, and apply and present techniques they learned in class.
Instructor: Professor Tom McAndrew

BSTA 103 | Population Health Data Science II Algorithms Lab | 1 cr.
Section 010 | R 19:15 - 21:55 | CRN: 45368 (to be taken with BSTA 101-010)
Section 011 | F 10:45 - 13:25 | CRN: 45369 (to be taken with BSTA 101-010)
Students will apply regression and machine learning models learned in BSTA101 Population Health Data Science II to health datasets using Python3. Datasets that students will study include: the National Health and Nutrition Examination Survey, influenza-like illness and confirmed positive cases of COVID-19 tracked by the Centers for Disease Control and Prevention, and the National Health Interview Survey. Lab is to be taken concurrently with lecture (BSTA 101 Population Health Data Science II).
Instructor(s): Professor Thomas McAndrew

BSTA 101 | Population Health Data Science II | 3 cr.
Section 011 | MW 13:35 - 14:50 | CRN:45351 
In this course students expand their statistics and machine learning toolkit by learning how to compare univariate distributions, build traditional regression models for continuous and binary data, explore supervised learning methods such as: Tree-based learning, KNN/Collaborative filtering, and Feed forward Neural networks, and understand how to manipulate, ask, and answer questions from big datasets. Students will be expected to propose a population health project mid-semester, and apply and present techniques they learned in class.
Instructor: Professor John Hughes

BSTA 103 | Population Health Data Science II Algorithms Lab | 1 cr.
Section 012 | F 13:35 - 16:15 | CRN: 45370 (to be taken with BSTA 101-011)
Students will apply regression and machine learning models learned in BSTA101 Population Health Data Science II to health datasets using Python3. Datasets that students will study include: the National Health and Nutrition Examination Survey, influenza-like illness and confirmed positive cases of COVID-19 tracked by the Centers for Disease Control and Prevention, and the National Health Interview Survey. Lab is to be taken concurrently with lecture (BSTA 101 Population Health Data Science II).
Instructor(s): Professor John Hughes

CGH 001 | Community Health | 3 cr.
Section 010 | MW 10:45 - 12:00 | CRN: 45346
The interdisciplinary field of community health focuses on improving the health of communities through health promotion and disease prevention, education, policy development, and community empowerment. This course provides students with an overview of theoretical, methodological, and practical aspects of community health with a focus on working in diverse communities. Students will gain an understanding of how community-level health issues relate to broader contextual issues within the community and externally.
Instructor: Professor Michael Gusmano

CGH 004 | Introduction to Global Population Health | 3 cr.
Section 010 | MW 13:35 - 14:50 | CRN: 45353
In this course, students will receive an introduction to global population health. We begin with an analysis of the rise of the international community in addressing population health needs, and the international norms guiding healthcare delivery systems. We will also focus on healthcare delivery systems, innovations, and policy reforms in response to healthcare needs in several developing nations. Finally, students will understand the political, social, and more recent commercial determinants of population health in these countries.
Instructor: Professor Ed Gomez

CGH 007 | 7 Dimensions of Health & Wellness | 3cr.
Section 010 | MW 9:20 - 10:35 | CRN 45445
Much has been discussed in the public sphere about happiness and how an individual can achieve peak happiness. This course delves more holistically into health and wellness, moving beyond individual happiness and towards a multi-level understanding of how interactions with others and the environment impact the self. Using a multi-disciplinary approach, students will learn the seven dimensions of health and wellness, including physical, mental, emotional, spiritual, cultural, environmental, and community, and how they interact to create healthy people and communities.
Instructor: Professor Christine Daley

CGH 021 | Culture and Health | 3 cr.
Section 010 | MW 12:10 - 13:25 | CRN: 45347
This course will introduce students to the complex and dynamic relationship between culture and health in Western and non-Western populations, communities, and societies. Cross-cultural institutions such as economics, politics, kinship, religion, and language, and their roles in sickness and illness will be discussed. The relationship between traditional and modern healing systems will also be analyzed.
Instructor: Professor Sean Daley

CGH/POP 197 | What is the US Healthcare Ecosystem? | 3cr.
Section 010 | TR 13:35 - 15:50
This course examines the structure, functioning, financing, and performance of the U.S. healthcare system. It aims to provide a general overview of the relationships between health care consumers, providers, organizations, regulators, and payers. The course will cover the history of the U.S. healthcare system and the political and social environment in which it exists and compare it to systems from other countries. It is essential for individuals working in health care to understand the health care system and its dynamic environment, understand their role and their organization’s role within the healthcare system, and assess and respond effectively to policy changes and organizational strategies.
Instructor: Professor Albert Liu

EPI 196 | Introduction to Why | 3 cr.
Section 010 | TR 15:00 - 16:15 | CRN: 45363
An axiom of statistics is that correlation (or association) is not causation. However, to answer important questions about what factors influence health we must move towards understanding causation from association. This course will introduce causal thinking with applications focused on epidemiology, population health, and decision theory. Topics covered will include background in history and evolution of causal thinking, major models of causation, potential outcomes and counterfactuals, directed acyclic graphs, and other tools to assist with causal reasoning. POPH 001, POPH 002, and BSTA 001 recommended, but not required.
Instructor: Professor Hal Skinner

HIT 010 | Seminar: Design Thinking for Innovation in Health | 1 cr.
Section 011 | R 10:45 - 12:00 | CRN: 45359
Design Thinking is a human-centered design process used to identify problems and create actionable solutions. Students will be exposed to the process, and attitudes needed, to frame and reframe problems, challenge assumptions, access their creativity, and tell compelling stories to communicate their ideas. The emphasis is on learning by doing and focuses on practicing the 5 steps in Design Thinking: Empathize, Define, Ideate, Prototype, Test that can be applied to virtually any area where new solutions are needed.
Instructor: Professor Chris Kauzmann

POPH 001 | Introduction to Population Health | 4 cr.
Section 110 | TR 12:10 - 13:25 | CRN: 44400
Despite significant advances in medicine and public health, inequities in health persist. Understanding health on a population level is an approach that seeks to improve the health of the whole population, unravel variations in health outcomes, and to identify effective strategies for reducing or eliminating inequities. The purpose of this course is to provide students with an understanding of: 1) how population health defined and measured; and 2) the determinants of population health (from cell to society).
Instructor: Professor Fatima Wakeel

POPH 003 | Justice, Equity, and Ethics in Population Health | 3 cr.
Section 010 | MW 09:20 - 10:35 | CRN: 45354
The goal is to examine the historical and emerging issues in population health ethics. The course will introduce both the historical contexts and contemporary issues in population health dilemmas. Topics of interest include: 1) resource distribution and social justice; 2) self-sufficiency and paternalism; 3) health promotion & disease prevention; 4) patients’ right to privacy; 5) research integrity; and 6) newly emerging issues. Students will debate, research, and propose solutions and intervention strategies through group discussions, role play, and presentations.
Instructor: Professor Hyunok Choi

POPH 101 | History of Population Health | 3 cr.
Section 010 | MW 07:55 - 09:10 | CRN:44398
This course introduces students to the development of population health as a convergent science. Students will learn about the evolution of population health interests, normative beliefs in service delivery, and policy at the international and domestic level. The principles, ethical values and services enforced by law will be learned. Students will explore the initiatives, collectively achieved among various sectors, addressing the determinants of health. We conclude by addressing future population health challenges, such as the environment, non-communicable diseases, and inequalities.
Instructor: Professor Ed Gomez

POPH 105 | Intro to Maternal and Child Population Health | 3 cr.
Section 010 | TR 09:20 - 10:35 | CRN: 45203 
The course introduces the student to the Maternal and Child Health field. Students will examine the multi-dimensional determinants of maternal and child health issues using a Life Course approach. Students will also explore the roles of research, programs, policy, and advocacy in the reduction of maternal and child health disparities.
Instructor: Professor Fathima Wakeel

POPH 106 | Global Environment and Human Welfare | 3 cr.
Section 010 | MW 12:10 - 13:25 | CRN: 44392
This course investigates the present understanding of multiple pollution agents and their effects on human health and well-being. The students will examine the history, the emergence, the known risks from exposure to specific pollutants through multiple media (e.g. air, water, food) with a particular focus on air pollution. Through readings, discussions, and a project, students are expected to cultivate a critical understanding of the risks posed by environmental pollutants on human health and identify knowledge gaps.
Instructor: Professor Hyunok Choi

POPH 126 | Population Health and the Media | 3 cr.
Section 110 | M 15:00 - 16:15 & W 13:35 - 14:50 | CRN: 45348
Section 111 | M 15:00 - 16:15 & W 15:00 - 16:15 | CRN: 45349
This course explores the importance of the relationship that health organizations have with the media and the powerful role it can play in what a population deems important in public health. We will examine how mass media campaigns have been used to change behaviors: tobacco, alcohol and drug use; lowering risk factors for heart disease and diabetes; and even road safety to produce life-saving changes in large populations as well as to manage national and worldwide health crises.
Instructors: Joan Lunden

POPH 198 | Cancer in the Population | 3 cr.
Section 010 | TR 10:45 - 12:00 | CRN: 44558
This introductory course will give a broad overview of how cancer affects the health of the population. Concepts covered will include the basics of cancer biology, measures of the burden of cancer in the population, fundamental methods in cancer epidemiology, and the roles that prevention and control play in cancer-related health outcomes.
Instructor: Professor Hal Skinner

BIOE 396 | Bioengineering Applications in Machine Learning | 3 cr.
Section 010 | TR 09:20 - 10:35a | CRN: 45055
Introduction to machine learning and AI techniques as well as their applications in biomedical data quantification, prediction, and visualization. Topics include principles of bioengineering data modalities and systems, fundamentals of machine learning approaches for biomedical data analysis (such as denoising, standardization, statistical analysis, dimensionality reduction, predictive modeling, etc.), and computational tools for implementing AI methods. Prerequisites: MATH 205 and PHY 21.
Instructor: Prof. Yu Zhang
 

BSTA 001 | Population Health Data Science | 4 Credits

POPH 002 | Population Health Research Methods and Applications | 4 Credits

POPH 010 | Cultural Understanding and Population Health Seminar | 1 Credit

HIT 010 | Design Thinking for Innovation in Health Seminar | 1 Credit

POPH 096 | Global Cultures and Population Health | 3 Credits | (provisional course)

POPH 097 | Introduction to Global Population Health | 3 Credits | (provisional course)

POPH 103 | Biological Basis of Population Health | 4 Credits

POPH 198 | Principles of Community Based Participatory Research | 3 Credits | (provisional course)

EPI 304 | Methods in Epidemiology I | 4 Credits

POPH 350 |  Special Topics: Data into Action  | 3 Credits