IEEE VDS 2020 will be happening virtually on Monday, October 26, 2020. Check out the exciting lineup of keynotes and speakers. In order to attend, please register (for free) for IEEE VIS and get access to all events.
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 or insights from this abundance of data lies at the heart of 21st century discovery, which can be used to inform decisions, coordinate activities, optimize processes, improve products and services, as well as enhance productivity and innovation across a wide range of business and scientific problems.
Data science is the practice of deriving insights 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 four highly successful events, the sixth Symposium on Visualization in Data Science (VDS) will again be held at IEEE VIS 2020 in Salt Lake City, Utah. 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 firstname.lastname@example.org to get in touch with us.
Registration for VDS is included with registration for IEEE VIS and is handled through the VIS website.
- Adam Perer, Carnegie Mellon University
- Hendrik Strobelt, IBM Research AI