VRVis Forum | #148

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VRVis, Donau-City-Straße 11, 5.OG, 1220 Wien

The inquisitive Data Scientist: Facilitating Well-Informed Data Science through Visual Analytics, by Cagatay Turkay, University of London


With the increasing availability of computational data analysis and modelling tools that can be utilised out-of-the-box, the route from data to results is now much shorter. However, these advancements also come together their own limitations, and a data scientists need to be aware of the pitfalls and act carefully to question every observation and method used within each step of the data analysis process. Visual analytics approaches where interactive visualisations are coupled tightly with the algorithms offer effective methodologies in conducting data science in such inquisitive, rigorous ways. This talk will discuss how visual analytics can facilitate such practices and will look at examples of research on how data can be transformed and visualised creatively in multiple perspectives, on how comparisons can be made within different models, parameters, and within local and global solutions, and on how interaction is an enabler for such processes.


Cagatay Turkay is a Senior Lecturer in Applied Data Science at giCentre at the Computer Science Department of City, University of London. He received his PhD degree in Visualisation from University of Bergen, Norway in 2014 and served as a visiting research fellow at Harvard University in 2013. His research focuses on designing visualisations, interactions and computational methods to enable an effective combination of human and machine capabilities to facilitate data-intensive problem solving. He frequently publishes on visualisation journals such as IEEE TVCG, CGF, and IEEE CG&A, as well as journals in machine learning and data mining. He serves as a programme committee member for several international conferences including VIS and EuroVis, and part of the organising committee for IEEE VIS for 2017 and 2018, and serves as a guest editor for IEEE Computer Graphics and Applications, and an editorial board member for the Machine Learning and Knowledge Extraction journal.