The explosion of digital data created by mobile sensors, social media, surveillance, medical imaging, smart grids and the like—combined with new tools for analyzing it all—has brought us a Big Data era. We are facing the great challenges: how to deal with data which is more than we could actually understand and absorb and how to make efficient use of the huge volume of data? From both scientific and practical perspectives, research on "Data Science" goes beyond the contents of Big Data. Data Science can be generally regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from data. It should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from data.
The "International symposium/workshop on Dataology and Data Science" has been a platform for researchers from data and some practitioners from industry and government to share their ideas, research results and experiences on studying of data. From 2010 to 2013, it has been annually held in China where more than 300 scholars and industrial professionals from Australia, Canada, China, Japan, UK and USA attended.
Started from 2014, this platform has been transferred as the annual International Conference on Data Science (ICDS) in order to further expand the preliminary findings and exchanges on Data Science. The last ICDS series were held at Beijing, China (ICDS 2014), Sydney, Australia (ICDS 2015), Xian, China (ICDS 2016) and Shanghai, China (ICDS 2017). ICDS 2018 will be held at Beijing, China in June 8-9, 2018. Its theme will be: "Advancement of Data Science and Big Data Applications". The main topics, but not limited to, are as follows:
We will invite well-known international scholars and professionals in various related fields, both natural and social sciences, to join us for the development of Data Science at this conference and so on to fully explore methodologies on Data Science from different research aspects.
KeyNote
