Prerequisites: BIOS 506, BIOS 507, BIOS 510, and BIOS 511 or permission of instructor. This course provides an introduction to statistical concepts and methods related to the analysis of survival data. Topics include survival functions, hazard rates, types of censoring and truncation, life tables, log-rank tests, Cox regression models, and parametric regression models. The emphasis is on practical implementation of standard methods using SAS or R and interpretation of results.

Prerequisites: BIOS 507 or permission of instructor. This course introduces students to regression techniques commonly used in analyzing longitudinal and multilevel data that are frequently encountered in biomedical and public health research. This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course focuses on data analysis and interpretation. Students will gain practical experience using R for statistical computing.

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.

Course only for BIOS MPH and MSPH students. This course will cover topics dedicated to preparing students for collaborations with non-statisticians in public health and biomedical projects. Covered topics include best practices in data analysis (data inspection, summarization, exploration, visualization, hypothesis formulation, analysis method selection, result interpretation, result presentation etc.), and professionalism as a collaborative statistician. The students will work individually or together in small groups on projects and conduct peer review for each other?s work. In addition, each student will complete a series of milestones for setting up individual capstone/thesis project to be completed in the Spring semester.

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 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.

The purpose of the course is to help students with their capstone project in project management, documentation, manuscript writing, and oral/poster presentations while they conduct their independent project with their individual BIOS advisors. Students will learn how to document their research progress, conduct best practice on coding, peer-review each other's work, and write journal articles section by section through lectures and homework assignments. They will develop a manuscript based on their capstone project. At the end of the semester, each student will give an oral presentation on his/her capstone project. Each student will also make a poster on his/her capstone project. Students will receive feedbacks from their peers and instructors to improve their writing and presentation skills.

Master's thesis research.

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.

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.

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: 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.

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.

The purpose of this course is to provide you with practical training on how to work with data and perform data analytics using Excel.