BIO Fall Courses

BIO6000 Capstone I MS Biostatistics

Credits: 1 Offered: Fall

The Capstone is a required three-semester course for students in the MS in Biostatistics Program. It provides experience in the art of consulting and in the proper application of statistical techniques to clinical and translational research. Students will bring together the skills they have acquired in previous coursework and apply them to the consulting experience. Learning will take place by doing. In the Fall term, the capstone-related lectures and project will engage students in important discourse regarding data management and research ethics. Prerequisites: Must be enrolled in the MS in Biostatistics program.

BIO6100 Fundamentals of Epidemiology

Credits: 3 Offered: Fall

This course provides a rigorous introduction to epidemiology for students in the first trimester of the MS in Biostatistics program. Topics covered include: an introductory overview of epidemiology, common measures of health outcome frequencies and associations, appropriate construction of an epidemiologic hypothesis, causal inferences, common epidemiologic study designs, error and bias in epidemiologic studies, confounding and effect modification, critique review and evaluation of published studies, ethics and reproducibility in epidemiologic research.

BIO6300 Introduction to R Programming

Credits: 2 Offered: Fall

In this course, students will gain a comprehensive, hands-on, introduction to statistical computing for data management and statistical analysis in R, a free, open source, statistical software. This course is geared towards students interested in becoming skilled and efficient data analysts in the biomedical, public health, or clinical and translational sectors. This course presumes prior or concurrent enrollment in a graduate-level introductory biostatistics course. Topics covered in this course include: basic commands, functions, and operations for vectors, matrices, and data frames, debugging, loops, data management, data visualization, and univariate and bivariate analyses. Pre- Students must have significant, minimum scripting-level, programming experience with demonstrated productivity in one or more programming languages (python preferred, but R and Matlab acceptable). Students with only toy-model programming experience will find the course immediately overwhelming. Specific mathematical or statistical expertise is not required, but college-level mastery of basic mathematical and statistical knowledge of fundamental concepts should be obtained prior to starting class. Such concepts include basic calculus, linear algebra and probability distributions. If none of these prerequisites are available, attending one or more of the following courses is required: Course: BMI1005-1007 (all modules) Computer Systems Course: BSR1803 Systems Biology: Biomedical Modeling Course: BMI2005 Introduction to Algorithms Course: BIO6300 Introduction to R programming ++

BIO6400 Biostatistics for Biomedical Research

Credits:3 Offered: Fall

This course covers the basic tools for the collection, analysis, and presentation of data in all areas of basics, clinical and translational research. Central to these skills is assessing the impact of chance and variability on the interpretation of research findings and subsequent implications on the understanding of disease mechanisms, drug discovery and development, and applications to clinical practice. Topics covered include: general principles of study design including internal and external validity; probability and sampling distributions, theory of confidence intervals and hypothesis testing; review of methods for comparison of discrete and continuous data including one-sample and two-sample tests, correlation analysis, linear regression, sample size and power. Additionally, students will learn to apply their statistical knowledge to complex real-world challenges, while gaining introductory statistical computing proficiency in R and SAS. Prerequisites: Algebra Required for MS in Biostatistics, students. All other students must take a placement test.

BIO6500 Probability and Inference I

Credits: 3 Offered: Fall

This course covers basic material in Probability Theory, which is necessary for all work in Biostatistics, especially as a foundation for Statistical Inference. We will introduce the basic terminology and concepts of probability theory, including sample and outcome spaces, random variables, discrete distributions and probability density functions. Students will also learn fundamental properties of the most important discrete and continuous probability distributions, expectations, moment generating functions, conditional probability and conditional expectations, multivariate distributions, laws of large numbers, and the central limit theorem. This course is a prerequisite for the Probability and Inference II course. Strong analytical and quantitative skills are required to successfully master the material covered in this course.