Academics

Division of Biostatistics and Bioinformatics

Biostatistics is an essential tool for unmasking patterns and quantifying relationships in population health data, and for evaluating the effect of treatments, interventions and policies on human health. As computational and biological technology advances, we gain the ability to generate and store huge amounts of data. This has led to a greater need for bioinformatics tools to learn new insights from such data. In the division of biostatistics and bioinformatics we use this data to understand and improve health policy, to conduct biomedical research, and to properly analyze all forms of epidemiological and clinical data. We also develop new techniques using the latest data science and machine learning tools for bioinformatics, to understand the huge volume of new data becoming available in this field.

Courses Offered by Division Faculty

Biostatistics I

This course will introduce the concepts of different study designs that are commonly used in health science research. Different descriptive statistical methods will be discussed in the course so that students are able to use appropriate statistical tools to analyze different types of data. The last part of the course will deal with statistical inference, which will help the students interpreting their data in terms of the concerned population. (Required Course)

Biostatistics I Practicum

In the practical course of Biostatistics I, one of the statistical programming languages (preferably R) will be introduced. At the end of this course, students are expected to have the skill to use this programming language independently to interpret descriptive and inferential statistical tools. (Elective Course)

Biostatistics II

Identifying important factors for the response of interest (which could be quantitative or qualitative) is one of the goals of health science research. In this context, the Biostatistics II course will deal with regression models that are appropriate for different types of responses. A brief introduction to the parametric and nonparametric methods of analyzing time-to-event data (e.g. survival analysis) is also provided in the course, because this type of data is often observed in different health science studies. (Elective Course)

Biostatistics II Practicum

In the practical Biostatistics II course, real life data will be used to understand the topics covered in the course (e.g. regression models, survival analysis, etc.). The statistical programming language introduced in the practical Biostatistics I course will also be used in this course. The expectation is that, at the end of this course, students will be able to analyze data so that they can write/read report/papers independently. (Elective Course)

Health Informatics and Decision Making

This course will provide the essential skills necessary for students in developing a foundational knowledge of health information science, and the ability to comprehend, criticize and synthesize in related contexts. It will cover an overview of health information technology and examine how the core techniques involved in health informatics and decision science have been and can be applied in clinical settings. Upon the course completion, students will understand the value of evidence-based decision- making and be equipped to identify and carry out health informatics science studies. (Elective Course)