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.

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.

This course will provide a pragmatic and hands-on introduction to the Python programming language, with a focus on practical applications and projects, rather than theoretical topics. We cover data types, control flow, object-oriented programming, and graphical user interface-driven applications. Students will learn to work with packages, data structures, and tools for data science and cybersecurity. The examples and problems used in this course are drawn from diverse areas such as text processing, simple graphics creation and image manipulation, HTML and web programming, and genomics.

This course will provide a pragmatic and hands-on introduction to the Python programming language, with a focus on practical applications and projects, rather than theoretical topics. We cover data types, control flow, object-oriented programming, and graphical user interface-driven applications. Students will learn to work with packages, data structures, and tools for data science and cybersecurity. The examples and problems used in this course are drawn from diverse areas such as text processing, simple graphics creation and image manipulation, HTML and web programming, and genomics.

Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings.

Prerequisites: BIOS 500 or permission of instructor. For EPI students Only taken in the spring semester of their first year. The course covers fundamental concepts in applied simple and multiple linear regression analyses, one- and two-way analysis of variance and binary logistic regression. Concepts in survival analysis will also be introduced. Students will learn when and how to apply these methods. The emphasis will be on practical data analysis skills rather than statistical theory; however, wherever possible and feasible, mathematical details of regression models will be presented. In-class data analysis examples will employ SAS and R software. Homework assignments, quizzes and exams will include data analyses using SAS and R, as well as other questions designed to reinforce concepts and assess foundational competencies. Teaching assistant office hours will consist of organized review/recitation sessions, and will also include opportunities for student questions.

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.

Provides an in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.

Master's thesis research.

Generalized inverse of a matrix; vectors of random variables; multivariate normal distribution; distribution theory for quadratic forms of normal random variable; fitting the general linear models by least squares; design matrix of less than full rank; estimation with linear restrictions; estimable functions; hypothesis testing in linear regression; and simultaneous interval estimation. Prerequisites: BIOS 507, BIOS 511, and a course in matrix algebra.