Abstract: Big Data is a collection of data so large, so complex, so distributed, and growing so fast (or 5Vs- volume, variety, velocity, veracity, and vinculation). It has been known for unlocking new sources of economic values, providing fresh insights into sciences, and assisting on policy making. The challenges of the Big Data Analytics (BDA) are that too often, researcher’s choice of analytic approach is dictated and constrained by available resources because of a lack of knowledge and/or understanding of available computer hardware, software and methodologies; unware existed similar successful application cases; and unconscious of better analytic methods, tools and resources. In addition, the Big Data skilled people are highly demanded. Thus, the objective of this presentation is to discuss the opportunities and challenges of mining health Big Data, and current status of Big Data Education. Bio: Dr. Alex Kuo holds a PhD from the Department of Computer Science, University of Nottingham, UK. He is a full time associate professor at the School of Health Information Science, University of Victoria (UVic). He was a visiting scholar at the Center for Expanded Data Annotation and Retrieval (CEDAR), School of Medicine, Stanford University, USA (2016), and the Electronic Commerce Resource Centre (ECRC), Georgia Tech (2000). He is the chair of the IEEE Big Data Education Track at the Big Data Initiative (BDI), and the chair of Special Interest Group on Big Data for Healthcare, Medicine and Biology at IEEE Technical Committee on Big Data. With over 20 years of programming and data analysis practical as well as research experience, he has over 160 peer-reviewed publications. His research interests include Cloud Computing & Big Data application to healthcare, health data interoperability, health database & data warehousing, data mining application in healthcare, e-health and clinical decision support system.