Introduction to Big Data Visual Analytics (HPTM 6284)
Course Director: Suresh K. Bhavnani
The accelerated growth and complexity of biomedical data far exceeds our cognitive abilities to exploit it for the prevention, diagnosis and treatment of diseases. A promising approach to bridge this gap is through the emerging field of visual analytics defined as the "science of analytical reasoning facilitated by interactive visual interfaces." This course provides the theoretical foundations and practical methods related to visual analytics focused towards the analysis and comprehension of large and complex biomedical datasets (e.g., genomic data, and electronic health records). The theoretical foundations will focus on principles related to cognition, computation and graphic design. The practical methods will focus on hands-on experience in using commercial applications and research protypes which integrate machine learning with visual analytics, neither requiring knowledge of programming. Through a required project, students will have the opportunity to integrate their theoretical and practical knowledge of big data visual analytics to analyze, comprehend, and present complex patterns in a large biomedical dataset.
1. Understand the motivation and theoretical foundations underlying visual analytics
2. Learn how to analyze, comprehend, and present patterns in biomedical data using visual analytics.
Location and Times
Classes will be held twice a week on Tuesdays and Thursdays from 3pm-5pm (except on holidays when it will be held the on the next working day) starting July 2nd, 2019 in the Discovery and Innovations through Visual Analytics Laboratory (DIVA Lab), Research Building 6, Room 6.168.
Biostatistics - BBSC 6222 or Interprofessional Translation Design Research - HPTM 6295, or with the permission of the instructor