Data Science AI Engineering

3 Credits · Level: Graduate

As a recent hot topic, big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time, and therefore, specialized and advanced paradigms, architectures, and analytical methodologies are necessary. Biomedical field is one of the most popular areas that generates big data which are typically collected from multiple sources and distributed from multiple sites. Statistical and computational skills are essential to analyze and extract knowledge from massive data. This course is designed for graduate students looking to acquire additional statistical and computational concepts, theories as well as skills beyond other informatics course(s) in the BIST curriculum. The course is divided into 4 modules with each module focusing on a specialized topic in biomedical data science taught by faculty with research interests and expertise in that specific research area. The goal is to introduce students to the use of cutting edge methodologies and tools in biomedical data science that have current broad applications on processing biomedical research and health care data.