Enables students to apply the principles and methods learned in an academic setting through the preparation of a scholarly document embodying original research applicable to public health, incorporating a research question that has been successfully evaluated with appropriate analytical techniques and is potentially publishable or has potential public health impact.

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.

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 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.

This course is the culminating experience of the data science certificate program and is to be taken in the spring semester of second year. The course must be taken by certificate-enrolled students in addition to any degree-required integrated learning experience (ILE) requirements. The course provides a review of current topics of interest in data science, helps prepare students for the data science job market, and involves a culminating data science project that relates to students' degree-required ILE. The first several meetings of this course focus on helping students identify suitable data science products and planning for the skills and tools that are needed to complete the ILE-related requirements for the data science certificate. Subsequent classes will cover modern topics in data science (e.g., R Shiny, communicating with diverse audiences, software unit testing, data sharing and privacy) and lectures on preparations for applying for data science-related jobs.

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.

PRE-REQ: DrPH Only. This course focuses on the development of a community intervention or program designed to address a public health issue. Students will select a public health challenge and develop a system-level program plan that takes into account cultural value and practices.

PRE-REQ: DrPH Only. This course teaches principles and methods of public health surveillance and its implications in public health practice. It concentrates on skill development in areas including the establishment of a public health surveillance program, the collation and analysis of data, data implications, program evaluation research, preparation and distribution of a surveillance report. The course also helps students recognize the importance of a direct association between a public health surveillance program and a public health action.