Vision
Transformations in many fields are enabled by rapid advances in our ability to acquire and generate data. The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. Extracting knowledge from this abundance of data lies at the heart of 21st century discovery.
Data science is the practice of deriving insight from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data management, visualization, and HCI. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond.
Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. After two highly successful events, the third Symposium on Visualization in Data Science (VDS) will again be held at IEEE VIS 2017 in Phoenix, Arizona, USA. VDS will bring together domain scientists and methods researchers (including visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in visualization in data science.
Contact & Registration
Please use vds@ieeevis.org to get in touch with us.
Registration for VDS is included with registration for IEEE VIS and is handled through the VIS website.
Chairs
- Luke Bornn, Simon Fraser University
- Alexander Lex, University of Utah
- Torsten Möller, Universität Wien
Steering Committee
- Daniel Keim, University of Konstanz
- Hanspeter Pfister, Harvard University
- Claudio Silva, New York University
- Marc Streit, Johannes Kepler University Linz