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.

Prerequisites: BIOS 500 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 and data definition statements. Homework will be done using databases running in MySQL which students install on their machines and proc SQL in SAS. Students develop a real-world database project using MySQL during the course.

Prerequisites: BIOS 500 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.

EH department students only. Required foundation course for students in all master's programs administered by Department of Environmental Health. Introduces students to major topics in environmental health, including mechanisms of toxicity, pesticides and other chemicals, children's health, WASH (water, sanitation, and hygiene), infectious disease, air pollution, climate change, and planetary health. Describes tools used to understand these EH topics, such as exposure science, epidemiology, toxicology, biomarkers/omics, risk assessment, implementation science, and policy.

Prerequisites: college-level biology and chemistry or instructor's permission. The goal of this course is to introduce the student to the basic principles of toxicology. Humans are exposed to a variety of dangerous substances through occupational and environmental exposures. In order to interpret the public health implications of these exposures one must have a good understanding of how these compounds get into the body, how they are processed in the body, and how they damage particular organ systems. To accomplish this, students will gain practical knowledge of the workings of specific organ systems and will be able to identify particular environmental chemicals and their mechanisms of action that underlie organ toxicity. This information will be conveyed through lecture material and reinforced by relevant readings, in-class discussion, and additional assignments that are focused on ensuring that the toxicological topics are further evaluated and considered in the context of current environmental and human health concerns and do not simply exist as standalone facts.

Surveys the general principles and practices of environmental health risk assessment for toxic exposures in the environment and interactions with other factors contributing to human health risks. A variety of case studies will be used to demonstrate the basic methods and results of risk assessment, including estimation/evaluation of potential risk based on empirical evidence (e.g., laboratory animal studies, epidemiological studies), hazard and dose-response assessment for regulatory decisions, and uncertainty analysis and risk communication. Students will be introduced to and use key tools used in quantitative risk assessment.

This elective course provides students with an overview of systems biology, genetics, epigenomics, and transcriptomics, within the context of environmental health. We will cover policy and translational implications and teach the underlying biological principles driving these analyses, laboratory methods involved, analytic approaches, and epidemiologic considerations. Upon completion of this course, students should be better equipped to read and interpret the scientific literature utilizing these methods and begin to consider how these approaches could be included in their own research.

Prerequisites: Students should have taken BIOS 500 and EPI 530. It is preferred that students also take BIOS 501 or BIOS 591P. Students should be comfortable using R. While not required, it is preferable that students take BIOS 544 concurrently or prior to taking this course. In the Methods for Environmental Mixtures course, students will learn the importance of evaluating environmental exposures as mixtures, as well as an overview of selected environmental mixture methods and data analysis techniques commonly used in public health research. This course focuses on developing an understanding of when to use a specific method, the pros and cons of different approaches, and hands-on applications of environmental mixture methods in R. The course is an elective that is open to second year MPH students and PhD students. It is required that students bring their laptops to class.

This course introduces basic concepts underpinning research and project design in environmental health. Students will learn of integrative learning experience (ILE) project types in environmental health, identify and/or refine their individual ILE project topics, develop key elements required for proposing work on their project topic, and demonstrate project feasibility by producing preliminary results. Throughout, students will develop and apply their writing skills and participate in providing feedback to peers. By the end of the course, students will submit a full written ILE proposal and a video trailer summarizing their proposed project in visual format.

The course provides a productive, supportive and critical environment for the completion of integrative learning experiences (ILE). EH skills gained during the MPH program are applied and integrated. Students will submit a completed ILE paper that describes the justification, methodologies, findings, and products of their ILE project, as well as a poster presentation that summarizes the highlights of their project.

An Applied Practice Experience (APE) is a unique opportunity that enables students to apply practical skills and knowledge learned through coursework to a professional public health setting that complements the student's interests and career goals. The APE must be supervised by a Field Supervisor and requires approval from an APE Advisor designated by the student's academic department at RSPH.

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.

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 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.

Prerequisite: EH 520 or instructor?s permission. This course is focused on understanding and evaluating the targets, molecular mechanisms, and physiological effects of specific environmental chemicals on the nervous system. This knowledge will be supplemented through outside readings and class discussions that serve to support the students? understanding of the material and provide them with a real-world perspective of neurotoxicology.

The health effects of environmental chemicals depend on their internal and metabolite concentrations in target tissues. Predicting internal exposures requires a physiologically based toxicokinetic (PBTK) modeling approach that mechanistically simulates the absorption, distribution, metabolism, and elimination (ADME) processes of xenobiotics in the human body. PBTK modeling is increasingly used in environmental health risk assessment. This introductory course teaches the fundamental concepts and simulation techniques of PBTK modeling for internal exposure predictions. Offered in conjunction with EHS/IBS 720.

This course presents the fundamental concepts of biomarkers of exposure to environmental chemicals including relevant clinical markers (e.g., inflammation or injury markers). The course introduces students to both quantitative and qualitative biomarker measurements and presents and interpretive framework for using biomarker data. Students will develop proficiency in applying the principles of exposure science to characterize and quantify environmental exposures.

Prerequisites: at least one GIS class (INFO 530) or equivalent. Geospatial information collected from satellite remote sensing has become a powerful tool in environmental and public health science and policy making. However, public health researchers usually lack training to benefit from this rapidly evolving technology. This computer lab-based course provides students with the theoretical basis and refined understanding of satellite remote sensing technologies, and tools for geospatial data analysis. Students will learn (1) the history, terminology and data structure of both land and atmospheric remote sensing such as those from MODIS and Landsat, and (2) the strategies and techniques to analyze geospatial data in advanced software packages. Various case studies and lab exercises help students overcome the initial hurdle to the effective use of satellite data in land use change and air pollution characterization, climate change and other areas related to public health. The final project allows the students to apply satellite data together with other information to solve a problem of their interest.

Additional in-depth computer exercises to EH 587; must enroll concurrently with EH 587. Enroll in EH 587 first before enrolling in EH 587L.

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.

Prerequisites: BIOS 500 and EPI 552 or instructor permission, Knowledge of R is recommended. Genomic epidemiology is an increasingly important approach to studying disease risks in populations. This course will introduce the basic genetic principles as they apply to the identification of genetic variations associated with disease; illustrate the population and quantitative genetic concepts that are necessary to study the relationship between genetic variation and disease variation in populations; and provide hands-on experience to address the analytical needs for conducting genomic epidemiologic research. Studentswill gain experience with R and PLINK using high dimensional genetic data.