For non-BIOS Students Only. The goal of the course is to will provide an introduction to R in organizing, analyzing, and visualizing data. Once you've completed this course you'll be able to enter, save, retrieve, summarize, display and analyze data.

For BIOS Students Only. This course covers the basic contents of R programming with applications on statistical data analysis. Topics include data types, language syntax, graphics packages, debugging, the tidy verse, efficient programming and package creation.

Prerequisites: BIOS 500, BIOS 506, BIOS 508 or permission of instructor. In this course, you'll learn about the basic structure of relational databases and how to read and write simple and complex SQL statements and advanced data manipulation techniques. By the end of this course, you'll have a solid working knowledge of structured query language. You'll feel confident in your ability to write SQL queries to create tables; retrieve data from single or multiple tables; delete, insert, and update data in a database; and gather significant statistics from data stored in a database. This course will teach key concepts of Structured Query Language (SQL), and gain a solid working knowledge of this powerful and universal database programming language. This course provides a comprehensive introduction to the language of relational databases: Structured Query Language (SQL). Topics covered include: Entity-Relationship modeling, the Relational Model, the SQL language: data retrieval statements, data manipulation.

Prerequisites: BIOS 500, 506, or 508 and (BIOS 544 or BIOS 545 or EPI 534) or permission of instructor. The elective course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using R as the programming language.

Prerequisites: BIOS 544 or BIOS 545, R programming experience needed or permission of the instructor. This course is an elective for Masters and PhD students interested in learning some fundamental tools used in modern data science. Together, the tools covered in the course will provide the ability to develop fully reproducible pipelines for data analysis, from data processing and cleaning to analysis to result tables and summaries. By the end of the course students will have learned the tools necessary to: develop reproducible workflows collaboratively (using version control based on Git/GitHub), execute these workflows on a local computer (using command line operations, RMarkdown, and GNU Makefiles), execute the workflows in a containerized environment allowing end-to-end reproducibility (using Docker), and execute the workflow in a cloud environment (using Amazon Web Services EC2 and S3 services). Along the way, we will cover a few other tools for data science including best coding practices, basic python, software unit testing, and continuous integration services.

Prerequisites: None. GH511 is the first in the two-semester Program Cycle sequence and is typically taken in the student's second semester. This course will provide students with theoretical principles and practical skills for designing and managing global health programs and projects. Sessions will focus on core activities following the project life cycle, including community engagement, formative research, situational analysis, theory of change, project design, principles of project and financial management, and ethical considerations and challenges. This course uses a variety of approaches to foster the development of practical skills in program design and management including lectures, interactive group sessions, discussions with experts, and task-based assignments. This course is a prerequisite for GH512 Program Cycle 2: Monitoring and Evaluation of Global Health Programs. The course is taken for a letter grade.

Provides students with the technical skills to conceptualize and design process and impact evaluations of international public health programs or projects. Helps students understand the role of monitoring and evaluation in policy analysis, planning, program design and management.

Prerequisites: BIOS 501 or permission of instructor. This is the overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics. This course will be co-taught by all BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and hot areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.

Prerequisites: BIOS 500, 506, or 508. This class is designed to cover the concepts and implementations of up-to-date analytic methodologies and strategies in observational studies, and to equip the students with the mindset and essential tools to handle data from observational research either for prediction (statistical learning) or causal inference. Propensity score methods, establishing/validating prediction models, risk stratification, the guidance of Good Research Practice, etc. will be illustrated along with real-life projects and backed up by the recent literatures.

Prerequisites: BIOS 501 or equivalents and basic programming in R or permission of the instuctor. This course covers the basics of microarray and second-generation sequencing data analysis using R/BioConductor and other open source software. Topics include gene expression microarray, RNA-seq, ChIP-seq and general DNA sequence analyses. We will introduce technologies, data characteristics, statistical challenges, existing methods and potential research topics. Students will also learn to use proper Bioconductor packages and other open source software to analyze different types of data and deliver biologically interpretable results.

The course introduces the use of geographic information systems (GIS) in the analysis of public health data. We develop GIS skills through homework, quizzes, and a case study. Specific skills include map layouts, visualization, and basic GIS operations such as buffering, layering, summarizing, geocoding, digitizing and spatial queries.

Prerequisites: INFO DATA 530 or permission of the instructor. The course continues the use of geographic information systems (GIS) in the analysis of public health data and adds more advanced features. We develop GIS skills through homework, quizzes and a final project, and particularly build upon the skills learned in INFO 530 such as map layouts, visualization, basic spatial statistics, and basic GIS operations such as buffering, layering, summarizing, geocoding, digitizing and spatial queries. We add new topics such as raster analysis open source GIS, (qgis), geo databases, story maps, and making maps in R.

Prerequisites: BIOS 544 or BIOS 545. This course will teach students to use data visualizations to analyze public health, medical, and biological sciences data and communicate information derived from these data to various audiences. Students will learn key concepts and methods in creating data visualizations and put them into practice with hands on assignments creating data visualization and critiquing public health visualizations. Multidisciplinary review and feedback on student designs can help to improve the quality and effectiveness of student visualization, therefore students will often work in pairs or groups.

Prerequisites EPI 530, BIOS 500, EPI 534 and BIOS 591P or BIOS 501 concurrent. This course develops epidemiologic concepts introduced in EPI 530: Epidemiologic Methods I, providing a more advanced discussion of issues related to causality, bias, study design, interaction, effect modification and mediation. It will also provide opportunities for the application of these examples via analysis of epidemiologic data.

Prerequisites EPI 530, BIOS 500, EPI 534, and BIOS 591P concurrent. MSPH and PhD students only.

This course builds on the fundamental epidemiologic concepts introduced in EPI 530: Epidemiologic Methods I. Specifically, causality, bias (including confounding, information bias, and selection bias), and concepts of mediation and interaction will be revisited in greater depth. By the end of the course, students will be able to do the following: formulate research questions to evaluate causality; evaluate the strengths and limitations of epidemiologic studies; assess how the strengths and limitations of a study affect interpretation of study results; utilize epidemiologic methods to address confounding; identify epidemiologic methods to address selection bias and information bias; and calculate measures to assess interaction.

This course aims to provide students with an understanding of key aspects of the design, administration and function of health systems and their implications for social justice and equity. Health systems are the primary vehicles through which countries deliver health services to their populations. The course aims to equip students with an understanding of how responsibility, authority and accountability for health systems is created and managed, and how health systems are financed and administered, including the roles and implications of private sector involvement and profit in health systems. And the course examines the complex challenges of measuring the performance of health systems, with special attention to the distinction between equality and equity as organizing concepts for improving the fairness and effectiveness of health systems.

The course aims to introduce students to methods for translating scientific knowledge into real world practice and policy. The course covers topics around identifying and appraising the evidence base, assessing and addressing barriers that impede implementation of proven interventions, designing innovative solutions and studies to test these, and concepts of decision science to promote implementation and sustainability of proven interventions. Throughout the course, students are exposed to case studies of global health interventions which illustrate implementation science concepts while evoking discussion and critical thinking.

This course provides an introduction to the collection of quantitative, representative data. Taking an applied approach, we cover the entire process of designing a study, including instrument design, sampling methods, budgeting and training, fieldwork components, and coding and editing of data. The focus is on collecting data in less-developed countries. Students develop their own surveys and accompanying methods proposals, which they may use for their Applied Practice Experience or other projects.

The course aims to introduce students to the pervasiveness and complexity of ethical challenges in global health. The goal of this course is to provide students with knowledge, skills and opportunities to critically examine and address ethical challenges associated with key aspects of global health. The course aims to complement other global health and public health courses by emphasizing critical analysis of the ethical and practical implications of global health and the assumptions, conventions, and practices that dominate the field. Given the unique impact and global challenges of the COVID-19 pandemic, the course will draw on cases and current ethical controversies associated with COVID-19 to examine some of the key ideas and concepts in global health ethics. Through the assigned readings, course assignments and interactions with guest speakers, students will be challenged to develop conceptual thinking and problem-solving skills relevant to four of the main professional activities associated with practice of global health ethics: (1) providing global health organizations with diagnoses of ethical challenges that arise within their portfolios, i.e., what is the nature of the ethical challenge; what is the best way to conceptualize and understood the challenge; (2) provide advice and guidance on how to address ethical challenges in creative and practical ways; (3) expert committee and panel reviews of policies or proposals; and (4) thought partnership and deliberation with global health organizations to help them design, manage and evaluate global health projects, programs, policies and management practices to ensure they meet the highest ethical standards. Cross-listed with BIOETH 505.

Provides an introduction to the basic scientific epidemiologic, economic, programmatic, and political aspects of vaccines and immunization. Emphases immunizations in the developing world, with examples also drawn from US experience. Cross-listed with EPI 566.