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Course Catalog

(number of credits in parenthesis)
Biostatistics II — Regression Analysis (3) HBDS 5008

Course Director: Samprit Banerjee, Ph.D., MStat 

Prerequisite: Biostatistics I

The focus of this course is theory and application of different types of regression analysis. Topics will include: linear regression, logistic regression, and cox proportional hazards regression. Additional topics will include coding of explanatory variables, residual diagnostics, model selection techniques, random effects and mixed models, and maximum likelihood estimation. Homework assignments will involve computation using the R statistical package.

Data Management (BDS) (3) HBDS 5021

Course Director: Debra D'Angelo, M.S.

This course covers tools that students will need to create, manage and maximize value from big databases. The emphasis is on design and implementation of relational databases and the use of Structured Query Language (SQL). At the end of this course, students will be able to explain the requirements for handling large and complex datasets; be able to design, build, and query a relational database; and understand how relational databases and big-data targeted tools complement one another.

Data Science II - Statistical Learning (3) HBDS 5014

Course Director: Samprit Banerjee, Ph.D., MStat 

The course starts with logistic regression and discriminant analysis with emphasis on classification and prediction. This course would cover some of more advanced topics such as regularized regression, resampling methods, tree-based methods and support vector machines.

Design and Analysis of Biomedical Studies (3) HBDS 5013

Course Director: Kathy (Xi) Zhou, Ph.D.

The course covers topics important in the application of statistical methods and relevant statistical software packages (primarily R) to biomedical studies, with an emphasis on applications in the design and analysis related to biomedical experiments, clinical trials and observational studies. The course uses real-world case studies to introduce commonly used experimental designs in biomedical research and discuss a variety of statistical methods and analytic tools for analyzing data generated from those studies. The course promotes good statistical/analytical practice through the introduction of several widely adopted reporting guidelines and tools for carrying out reproducible data analysis. The course aims to help students develop expertise in applying statistical methods and analytical tools, including developing their own R packages, to solve the design and data analysis challenges in biomedical studies and beyond.