BDSI-700. Biomedical Data Science and Informatics Seminar. This course is mandatory for students in Clemson-MUSC Biomedical Data Science and Informatics students. The overall goal of the course is to expose students to a broad range of concepts, theories, methods, and practices in biomedical data science and informatics, and the specific research topics pursued by the faculty in the program. The students will learn to comprehend and present scientific literature in this field. 1 c.h.
BDSI-701. Intro to Biomedical Informatics. This is an introductory course to provide students an overview of the biomedical informatics filed. Students will learn fundamental theories and concepts of bioinformatics, clinical research informatics, health informatics, consumer health informatics, and public health informatics. Students will learn informatics tools, techniques, and approaches for research and health care. The course is taught by a variety of informatics experts. The course is required for BDSI PhD students and is open to other students interested in understanding of biomedical informatics. No previous informatics or computer science experience is required. 3 c.h.
BDSI-8650. Data Mining. This course is delivered through Clemson University as CPCS 8650 as part of a joint Biomedical Data Science and Informatics program.Data mining has emerged as one of the most exciting and dynamic fields in computer science, bioinformatics, industrial engineering, etc. The driving force for data mining is the available of massive data that potentially contain valuable bits of hidden knowledge. Such data include consumer data, transaction histories, medical records, biological experiments, Web information, Network information, etc. Commercial enterprises have been quick to recognize the value of data mining; consequently, within the span of a few years, the software market for data mining has expanded to be in excess of tens of billions of dollars. This course is designed to provide graduate students with a broad knowledge in the design and use of data mining algorithms, exposure to data mining research, and hands-on practices in applying these ideas to a real-life situation. 3 c.h.
|Last Published with Edits:||September 21, 2017 10:42 AM|
|Last Comprehensive Review:||June 2017|